{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ChartBusters-Notebook",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wZcEQXq_o_1M",
        "colab_type": "text"
      },
      "source": [
        "# Chartbusters Prediction : Foretell The Popularity Of Songs\n",
        "<img src=https://www.machinehack.com/wp-content/uploads/2019/12/ChartbustersPrediction-scaled.jpg width=\"500\" height=\"300\"></img>\n",
        "\n",
        "One of MachineHack's customers strongly believes in technology and has recently backed up its platform using Machine Learning and Artificial Intelligence. Based on data collected from multiple sources on different songs and various artist attributes their customer is excited to challenge the MachineHack community.\n",
        "\n",
        "By analyzing the chartbusters data to predict the Views of songs, MachineHackers would advance the state of the current platform. This can help their customer understand user behaviour and personalize the user experience. In this hackathon, they challenge the MachineHackers to come up with a prediction algorithm that can predict the views for a given song.\n",
        "\n",
        "Can you predict how popular a song will be in the future?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I2XlVXUZpsCd",
        "colab_type": "text"
      },
      "source": [
        "### Import Libaries and Helper Functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gw2U4xf6oBZj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c341c73c-6a63-4c15-b04f-aae276b2e697"
      },
      "source": [
        "# Essentials\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import datetime\n",
        "import random\n",
        "!pip install -q tqdm\n",
        "from tqdm import tqdm\n",
        "\n",
        "# Plots\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "color = sns.color_palette()\n",
        "sns.set_style('darkgrid')\n",
        "plt.rcParams[\"figure.figsize\"] = (12,8)\n",
        "\n",
        "\n",
        "# Models\n",
        "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, AdaBoostRegressor, BaggingRegressor\n",
        "from sklearn.kernel_ridge import KernelRidge\n",
        "from sklearn.linear_model import Ridge, RidgeCV\n",
        "from sklearn.linear_model import ElasticNet, ElasticNetCV\n",
        "from sklearn.svm import SVR\n",
        "from mlxtend.regressor import StackingCVRegressor\n",
        "import lightgbm as lgb\n",
        "from lightgbm import LGBMRegressor\n",
        "from xgboost import XGBRegressor\n",
        "import xgboost as xgb\n",
        "\n",
        "# Stats\n",
        "from scipy.stats import skew, norm\n",
        "from scipy import stats\n",
        "from scipy.stats import norm, skew #for some statistics\n",
        "from scipy.special import boxcox1p\n",
        "from scipy.stats import boxcox_normmax\n",
        "\n",
        "# Misc\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.model_selection import KFold, cross_val_score\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import scale\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "from sklearn.decomposition import PCA\n",
        "from math import sqrt\n",
        "\n",
        "# Keras\n",
        "%tensorflow_version 1.x\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.wrappers.scikit_learn import KerasRegressor\n",
        "from keras import backend as K\n",
        "\n",
        "pd.set_option('display.max_columns', None)\n",
        "\n",
        "# Ignore warnings\n",
        "import warnings\n",
        "warnings.filterwarnings(action=\"ignore\")\n",
        "pd.options.display.max_seq_items = 8000\n",
        "pd.options.display.max_rows = 8000\n",
        "\n",
        "pd.set_option('display.float_format', lambda x: '{:.5f}'.format(x)) #Limiting floats output to 5 decimal points"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YcVe7w2Wp_ut",
        "colab_type": "text"
      },
      "source": [
        "### Load and Explore Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cqYR_EzyoF0-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "da88e8bd-7cf6-4d42-8308-cddb77768d68"
      },
      "source": [
        "train = pd.read_csv('Data_Train.csv', index_col=None)\n",
        "test = pd.read_csv('Data_Test.csv', index_col=None)\n",
        "print('Train shape:',train.shape)\n",
        "print('Test shape:',test.shape)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train shape: (78458, 11)\n",
            "Test shape: (19615, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y6rNjFZYoF6o",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "b29ae271-f172-468c-8c96-ed5188d49c87"
      },
      "source": [
        "train.tail()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unique_ID</th>\n",
              "      <th>Name</th>\n",
              "      <th>Genre</th>\n",
              "      <th>Country</th>\n",
              "      <th>Song_Name</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>Views</th>\n",
              "      <th>Comments</th>\n",
              "      <th>Likes</th>\n",
              "      <th>Popularity</th>\n",
              "      <th>Followers</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>78453</th>\n",
              "      <td>1144131</td>\n",
              "      <td>Resident Advisor</td>\n",
              "      <td>ambient</td>\n",
              "      <td>AU</td>\n",
              "      <td>RA.505 Josey Rebelle</td>\n",
              "      <td>2016-02-08 09:11:33.000000</td>\n",
              "      <td>12221</td>\n",
              "      <td>19</td>\n",
              "      <td>489</td>\n",
              "      <td>47</td>\n",
              "      <td>143726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78454</th>\n",
              "      <td>1415261</td>\n",
              "      <td>Trap Sounds</td>\n",
              "      <td>trap</td>\n",
              "      <td>AU</td>\n",
              "      <td>Drama We Cause - Lurtz [Exclusive]</td>\n",
              "      <td>2016-03-02 01:24:39.000000</td>\n",
              "      <td>8265</td>\n",
              "      <td>4</td>\n",
              "      <td>292</td>\n",
              "      <td>100</td>\n",
              "      <td>211419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78455</th>\n",
              "      <td>705157</td>\n",
              "      <td>Mixmag</td>\n",
              "      <td>electronic</td>\n",
              "      <td>AU</td>\n",
              "      <td>Premiere: ELLLL 'SKITTLES'</td>\n",
              "      <td>2019-02-11 13:15:05.000000</td>\n",
              "      <td>3621</td>\n",
              "      <td>1</td>\n",
              "      <td>157</td>\n",
              "      <td>31</td>\n",
              "      <td>1403057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78456</th>\n",
              "      <td>175337</td>\n",
              "      <td>DHA AM (Deep House London)</td>\n",
              "      <td>electronic</td>\n",
              "      <td>AU</td>\n",
              "      <td>Night Vision - DHL Mix #124</td>\n",
              "      <td>2017-01-18 10:10:12.000000</td>\n",
              "      <td>8896</td>\n",
              "      <td>24</td>\n",
              "      <td>371</td>\n",
              "      <td>89</td>\n",
              "      <td>143743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78457</th>\n",
              "      <td>501778</td>\n",
              "      <td>Jon Z ✅</td>\n",
              "      <td>all-music</td>\n",
              "      <td>AU</td>\n",
              "      <td>Energy Freestyle</td>\n",
              "      <td>2015-04-11 22:32:05.000000</td>\n",
              "      <td>102487</td>\n",
              "      <td>4</td>\n",
              "      <td>1,829</td>\n",
              "      <td>71</td>\n",
              "      <td>89084</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Unique_ID                        Name       Genre Country  \\\n",
              "78453    1144131            Resident Advisor     ambient      AU   \n",
              "78454    1415261                 Trap Sounds        trap      AU   \n",
              "78455     705157                      Mixmag  electronic      AU   \n",
              "78456     175337  DHA AM (Deep House London)  electronic      AU   \n",
              "78457     501778                     Jon Z ✅   all-music      AU   \n",
              "\n",
              "                                Song_Name                   Timestamp   Views  \\\n",
              "78453                RA.505 Josey Rebelle  2016-02-08 09:11:33.000000   12221   \n",
              "78454  Drama We Cause - Lurtz [Exclusive]  2016-03-02 01:24:39.000000    8265   \n",
              "78455          Premiere: ELLLL 'SKITTLES'  2019-02-11 13:15:05.000000    3621   \n",
              "78456         Night Vision - DHL Mix #124  2017-01-18 10:10:12.000000    8896   \n",
              "78457                    Energy Freestyle  2015-04-11 22:32:05.000000  102487   \n",
              "\n",
              "       Comments  Likes Popularity  Followers  \n",
              "78453        19    489         47     143726  \n",
              "78454         4    292        100     211419  \n",
              "78455         1    157         31    1403057  \n",
              "78456        24    371         89     143743  \n",
              "78457         4  1,829         71      89084  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wdt0JkdwoF3u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "e7e5237c-c5b5-4f88-d717-69ffe7075141"
      },
      "source": [
        "train.info()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 78458 entries, 0 to 78457\n",
            "Data columns (total 11 columns):\n",
            "Unique_ID     78458 non-null int64\n",
            "Name          78458 non-null object\n",
            "Genre         78458 non-null object\n",
            "Country       78458 non-null object\n",
            "Song_Name     78457 non-null object\n",
            "Timestamp     78458 non-null object\n",
            "Views         78458 non-null int64\n",
            "Comments      78458 non-null int64\n",
            "Likes         78458 non-null object\n",
            "Popularity    78458 non-null object\n",
            "Followers     78458 non-null int64\n",
            "dtypes: int64(4), object(7)\n",
            "memory usage: 6.6+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KjfBv45vqwtL",
        "colab_type": "text"
      },
      "source": [
        "**Note**: The target is the **Views** column. Since it is a continuous variable, this is a regression problem."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YO1z4qdjrotr",
        "colab_type": "text"
      },
      "source": [
        "### Visualizing and Potentionally Transforming the Target"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J3vYRSauoF9N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d4286ee6-9c23-468f-fc83-2199792eea99"
      },
      "source": [
        "# Plot the Target distribution \n",
        "sns.distplot(train['Views'] , fit=norm);\n",
        "\n",
        "# Get the fitted parameters used by the function\n",
        "(mu, sigma) = norm.fit(train['Views'])\n",
        "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
        "\n",
        "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)], loc='best')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Views distribution')\n",
        "\n",
        "#Create a corresponding QQ-plot\n",
        "fig = plt.figure()\n",
        "res = stats.probplot(train['Views'], plot=plt)\n",
        "plt.show()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " mu = 546968.64 and sigma = 3883060.66\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/he1Op1M5OTnexzk5OXI6ndUe0+02dOxYQY3XapZhC1FBYYnP44uKS3XsmMfC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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uqjJUUpIuiaX4oqVLzdO1tbX67LPP1NBwavHxMWPGRKwoAAAAhK6sLM1n8xhJ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\niIiIiJsouRYRERERcRMl1yIiIiIibvIfviPdx4hr40UAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W_zDoH4yrza0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "93f76a17-f20d-4b08-c684-dca8234c9149"
      },
      "source": [
        "# We use the numpy fuction log1p which  applies log(1+x) to all elements of the column\n",
        "# Plot the log-transformed distribution \n",
        "sns.distplot(np.log1p(train['Views']) , fit=norm);\n",
        "\n",
        "# Get the fitted parameters used by the function\n",
        "(mu, sigma) = norm.fit(np.log1p(train['Views']))\n",
        "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
        "\n",
        "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)], loc='best')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Log-transformed Views distribution')\n",
        "\n",
        "# Create a corresponding QQ-plot\n",
        "fig = plt.figure()\n",
        "res = stats.probplot(np.log1p(train['Views']), plot=plt)\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " mu = 10.07 and sigma = 2.73\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sCZNuIqJOLKe8FgWVKgwJ7GLUFUuaI5eIMXNoVxzNVHC0m4g6FSbdRESd2Mms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resigCdB+p66rcD9Z6X7MV6KbI4UgghxB/xv/Xf3SL96BTmQ2ZxFRnnKjmUbWRXRjEA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\nIYQQQohGJkm3EEIIIYQQjez/Acs8SilgbQn/AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BJE7mDb7gWvbmZybShUGMIJ2ahaxydlkqiYiF5tGjR//j2AUXXBCpt5MkqUrZ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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "64VUwBlfr0FT",
        "colab_type": "text"
      },
      "source": [
        "**Important Note**: The distribution is extremely right-skewed. Ordinarily, we would need to linearlize the distribution using a suitable transformation so that the fitted regression model performs well (an assumption of inferential statistics). A popular choice is the [log transformation](http://onlinestatbook.com/2/transformations/log.html).\n",
        "\n",
        "However, while running experiments, it was noted that fitted tree-based models performed *significantly* worse against the evaluation metric when the target was log-transformed. I'm curious as to why this is the case. One hypothesis, is that, while training, the model gives higher weights to non-predictive features when the target is transformed. It was empirically observed during experiments. Perhaps it is a peculiarity to do with this particular dataset.\n",
        "\n",
        "To conclude, we will leave the target as-is."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YiL90RWLtagb",
        "colab_type": "text"
      },
      "source": [
        "### Combine the Datasets for Data Cleaning and Feature Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yf4gfWLKoGAJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Drop the Target after storing the values for later\n",
        "Y_train = train.Views.values\n",
        "target = \"Views\"\n",
        "train.drop([target], axis=1, inplace=True)\n",
        "\n",
        "# Save the 'Unique_ID' column for later use\n",
        "train_ID = train['Unique_ID']\n",
        "test_ID = test['Unique_ID']\n",
        "\n",
        "# Drop the 'Unique_ID column since it is unnecessary for the analysis and prediction phase.\n",
        "train.drop(\"Unique_ID\", axis = 1, inplace = True)\n",
        "test.drop(\"Unique_ID\", axis = 1, inplace = True)\n",
        "\n",
        "# Combine the dataframes\n",
        "alldata = pd.concat([train, test], axis=0, sort=False, ignore_index=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7a8I5a6BuhVH",
        "colab_type": "text"
      },
      "source": [
        "### Data Cleaning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QxhSHhykuw0E",
        "colab_type": "text"
      },
      "source": [
        "**Note:** The Likes and Popularity columns substitute Ks and Ms for 1000s and 1000000s, respectively (e.g. 2500 is 2.5K). We need to find and convert those string representations into numeric values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EXqXTzFvoGCq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def clean_likes_popularity(data):\n",
        "    data = data.apply(lambda x: x.replace(',',''))\n",
        "    a = 'K'\n",
        "    b = 'M'\n",
        "    data = data.apply(lambda x: int(float(x.replace('K',''))*1000) if a in x else(int(float(x.replace('M',''))*1000000) if b in x else int(x)))\n",
        "    \n",
        "    return data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ygOSPTmeoGFC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "alldata['Likes'] = clean_likes_popularity(alldata['Likes'])\n",
        "alldata['Popularity'] = clean_likes_popularity(alldata['Popularity'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KbHUPKumoGJU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "alldata['Timestamp'] = pd.DatetimeIndex(alldata['Timestamp'])\n",
        "alldata['dayofweek'] = alldata['Timestamp'].dt.dayofweek\n",
        "alldata['dayofyear'] = alldata['Timestamp'].dt.dayofyear\n",
        "alldata['weekofyear'] = alldata['Timestamp'].dt.weekofyear\n",
        "alldata['Year'] = alldata['Timestamp'].dt.year\n",
        "alldata['Month'] = alldata['Timestamp'].dt.month\n",
        "alldata['Day'] = alldata['Timestamp'].dt.day\n",
        "alldata['Hour'] = alldata['Timestamp'].dt.hour\n",
        "alldata['Minute'] = alldata['Timestamp'].dt.minute\n",
        "alldata['Second'] = alldata['Timestamp'].dt.second"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OGkb6J7RoH9B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Mean-imputation of missing values\n",
        "alldata['Followers'] = pd.to_numeric(alldata['Followers'], errors='coerce')\n",
        "alldata['Likes'] = pd.to_numeric(alldata['Likes'], errors='coerce').fillna(pd.to_numeric(alldata['Likes'], errors='coerce').mean())\n",
        "alldata['Popularity'] = pd.to_numeric(alldata['Popularity'], errors='coerce').fillna(pd.to_numeric(alldata['Popularity'], errors='coerce').mean())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-yIz-tXU0SWD",
        "colab_type": "text"
      },
      "source": [
        "### Feature Analysis and Feature Engineering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7OcT8jRIoGLh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "08e055ec-e251-4150-bfe9-719115d84ca1"
      },
      "source": [
        "alldata.Song_Name.nunique(), alldata.shape[0]"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(98072, 98073)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HJ8uEd0DxWHM",
        "colab_type": "text"
      },
      "source": [
        "**Note:** The song names seem to be unique and may need to be dropped. We could later extract some TF-IDF & CountVectorizer vectors (or even GloVe embeddings if we're ambitious) from it. For now, we will instead choose create a new feature for the number of collaborating/featured artists on a song. This can be identified in the data with either 'FEAT', 'feat', or 'Feat'."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UWD7zrVYoGPj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Drop Song_Name\n",
        "alldata['Collaborators'] = alldata['Song_Name'].str.count('FEAT|feat|Feat')\n",
        "alldata.drop(columns=['Song_Name'], axis=1, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RGv_9LcKBr1a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "outputId": "7a290db0-2f0a-4a20-b9c2-8990d31850b3"
      },
      "source": [
        "# Clean up text using regex\n",
        "import re\n",
        "def clean_text(text):\n",
        "    text = text.lower()\n",
        "    text = re.sub(r'@[a-zA-Z0-9_]+', '', text)   \n",
        "    text = re.sub(r'https?://[A-Za-z0-9./]+', '', text)   \n",
        "    text = re.sub(r'www.[^ ]+', '', text)  \n",
        "    text = re.sub(r'[a-zA-Z0-9]*www[a-zA-Z0-9]*com[a-zA-Z0-9]*', '', text)  \n",
        "    text = re.sub(r'[^a-zA-Z]', ' ', text)   \n",
        "    text = [token for token in text.split() if len(token) > 2]\n",
        "    text = ' '.join(text)\n",
        "    return text\n",
        "\n",
        "alldata['Name'] = alldata['Name'].astype(str)\n",
        "alldata['Name'] = alldata['Name'].apply(clean_text)\n",
        "alldata['Name'] = alldata['Name'].astype('O')\n",
        "alldata.head()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Genre</th>\n",
              "      <th>Country</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>Comments</th>\n",
              "      <th>Likes</th>\n",
              "      <th>Popularity</th>\n",
              "      <th>Followers</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>dayofyear</th>\n",
              "      <th>weekofyear</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>Hour</th>\n",
              "      <th>Minute</th>\n",
              "      <th>Second</th>\n",
              "      <th>Collaborators</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>hardstyle</td>\n",
              "      <td>danceedm</td>\n",
              "      <td>AU</td>\n",
              "      <td>2018-03-30 15:24:45</td>\n",
              "      <td>4</td>\n",
              "      <td>499</td>\n",
              "      <td>97</td>\n",
              "      <td>119563</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>2018</td>\n",
              "      <td>3</td>\n",
              "      <td>30</td>\n",
              "      <td>15</td>\n",
              "      <td>24</td>\n",
              "      <td>45</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>aladdin</td>\n",
              "      <td>danceedm</td>\n",
              "      <td>AU</td>\n",
              "      <td>2016-06-20 05:58:52</td>\n",
              "      <td>17</td>\n",
              "      <td>49</td>\n",
              "      <td>17</td>\n",
              "      <td>2141</td>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>25</td>\n",
              "      <td>2016</td>\n",
              "      <td>6</td>\n",
              "      <td>20</td>\n",
              "      <td>5</td>\n",
              "      <td>58</td>\n",
              "      <td>52</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>maxximize air</td>\n",
              "      <td>danceedm</td>\n",
              "      <td>AU</td>\n",
              "      <td>2015-05-08 17:45:59</td>\n",
              "      <td>11</td>\n",
              "      <td>312</td>\n",
              "      <td>91</td>\n",
              "      <td>22248</td>\n",
              "      <td>4</td>\n",
              "      <td>128</td>\n",
              "      <td>19</td>\n",
              "      <td>2015</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>17</td>\n",
              "      <td>45</td>\n",
              "      <td>59</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>explode</td>\n",
              "      <td>rbsoul</td>\n",
              "      <td>AU</td>\n",
              "      <td>2017-06-08 23:50:03</td>\n",
              "      <td>2</td>\n",
              "      <td>2400</td>\n",
              "      <td>76</td>\n",
              "      <td>393655</td>\n",
              "      <td>3</td>\n",
              "      <td>159</td>\n",
              "      <td>23</td>\n",
              "      <td>2017</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>23</td>\n",
              "      <td>50</td>\n",
              "      <td>3</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tritonal</td>\n",
              "      <td>danceedm</td>\n",
              "      <td>AU</td>\n",
              "      <td>2016-09-17 20:50:19</td>\n",
              "      <td>81</td>\n",
              "      <td>3031</td>\n",
              "      <td>699</td>\n",
              "      <td>201030</td>\n",
              "      <td>5</td>\n",
              "      <td>261</td>\n",
              "      <td>37</td>\n",
              "      <td>2016</td>\n",
              "      <td>9</td>\n",
              "      <td>17</td>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
              "      <td>19</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Name     Genre Country           Timestamp  Comments  Likes  \\\n",
              "0      hardstyle  danceedm      AU 2018-03-30 15:24:45         4    499   \n",
              "1        aladdin  danceedm      AU 2016-06-20 05:58:52        17     49   \n",
              "2  maxximize air  danceedm      AU 2015-05-08 17:45:59        11    312   \n",
              "3        explode    rbsoul      AU 2017-06-08 23:50:03         2   2400   \n",
              "4       tritonal  danceedm      AU 2016-09-17 20:50:19        81   3031   \n",
              "\n",
              "   Popularity  Followers  dayofweek  dayofyear  weekofyear  Year  Month  Day  \\\n",
              "0          97     119563          4         89          13  2018      3   30   \n",
              "1          17       2141          0        172          25  2016      6   20   \n",
              "2          91      22248          4        128          19  2015      5    8   \n",
              "3          76     393655          3        159          23  2017      6    8   \n",
              "4         699     201030          5        261          37  2016      9   17   \n",
              "\n",
              "   Hour  Minute  Second  Collaborators  \n",
              "0    15      24      45        0.00000  \n",
              "1     5      58      52        0.00000  \n",
              "2    17      45      59        0.00000  \n",
              "3    23      50       3        0.00000  \n",
              "4    20      50      19        1.00000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q3AVm8gfoH_9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create interaction features. Note: They be correlated with many columns in our dataframe\n",
        "\n",
        "# Start with Likes in the numerator\n",
        "alldata['Likes_Followers'] = np.divide(alldata['Likes'], alldata['Followers'])\n",
        "alldata['Likes_Popularity'] = np.divide(alldata['Likes'], alldata['Popularity'])\n",
        "alldata['Likes_Comments'] = np.divide(alldata['Likes'], alldata['Comments'])\n",
        "\n",
        "# Followers\n",
        "alldata['Followers_Popularity'] = np.divide(alldata['Followers'], alldata['Popularity'])\n",
        "alldata['Followers_Comments'] = np.divide(alldata['Followers'], alldata['Comments'])\n",
        "alldata['Followers_Likes'] = np.divide(alldata['Followers'], alldata['Likes'])\n",
        "\n",
        "# Popularity\n",
        "alldata['Popularity_Followers'] = np.divide(alldata['Popularity'], alldata['Followers'])\n",
        "alldata['Popularity_Comments'] = np.divide(alldata['Popularity'], alldata['Comments'])\n",
        "alldata['Popularity_Likes'] = np.divide(alldata['Popularity'], alldata['Likes'])\n",
        "\n",
        "alldata['Total_Metrics'] = alldata['Likes'] + alldata['Popularity'] + alldata['Followers'] + alldata['Comments']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2E8V-A2B23Sf",
        "colab_type": "text"
      },
      "source": [
        "#### Aggregate Descriptive Stats of Features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fz0ekFaf3U8I",
        "colab_type": "text"
      },
      "source": [
        "**Note:** This feature engineering step is inspired by the [winning solution](https://github.com/chetanambi/Predicting-Food-Delivery-Time-Hackathon-by-IMS-Proschool/blob/master/predicting-food-delivery-time-catboost_0.83958.ipynb) of MachineHack: [Predict Food Delivery Time](https://www.machinehack.com/course/predicting-food-delivery-time-hackathon-by-ims-proschool/) by Chetan Ambi. Any aspiring competitive data science enthusiast will find winning solutions to previous hackathons an invaluable learning resource. I highly recommend it!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "anzL9RQEoIEw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "agg_func = {\n",
        "    'Likes': ['sum'],\n",
        "    'Followers': ['sum'],\n",
        "    'Popularity': ['sum'],\n",
        "    'Comments': ['sum']\n",
        "}\n",
        "agg_Year_Name = alldata.groupby(['Year','Name']).agg(agg_func)\n",
        "agg_Year_Name.columns = [ 'Year_Name_' + ('_'.join(col).strip()) for col in agg_Year_Name.columns.values]\n",
        "agg_Year_Name.reset_index(inplace=True)\n",
        "alldata = alldata.merge(agg_Year_Name, on=['Year','Name'], how='left')\n",
        "del agg_Year_Name"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iR_Po7f4oICb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "agg_func = {\n",
        "    'Likes': ['mean', 'median','sum','min','max'],\n",
        "    'Followers': ['mean', 'median','sum','min','max'],\n",
        "    'Popularity': ['mean', 'median','sum','min','max'],\n",
        "    'Comments': ['mean', 'median','sum','min','max']\n",
        "}\n",
        "agg_Name = alldata.groupby('Name').agg(agg_func)\n",
        "agg_Name.columns = [ 'Name' + ('_'.join(col).strip()) for col in agg_Name.columns.values]\n",
        "agg_Name.reset_index(inplace=True)\n",
        "alldata = alldata.merge(agg_Name, on=['Name'], how='left')\n",
        "del agg_Name"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pzrdRU3k6sOM",
        "colab_type": "text"
      },
      "source": [
        "### Encoding Categorical Features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YddG9IXZpk_K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def mean_likelihood(df, cat_var, target, alpha = 0.5):\n",
        "    P_c = df.groupby(cat_var)[target].transform('mean')\n",
        "    P_global = df[target].mean()\n",
        "    n_c = df.groupby(cat_var)[target].transform('count')\n",
        "    enc = (P_c*n_c + P_global*alpha)/(n_c + alpha)\n",
        "    temp = df[[cat_var]]\n",
        "    temp['enc'] = enc\n",
        "    return temp.groupby(cat_var).mean()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lLSK0foUplHa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ntrain = train_ID.shape[0]\n",
        "train_df = alldata[:ntrain]\n",
        "test_df = alldata[ntrain:]\n",
        "train_df['Views'] = Y_train"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5eHWpzc6plg7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Obtaining mean-encoded features\n",
        "# It was observed that performance suffers with target-encoded features. Future work should try regularization\n",
        "'''cat_vars = ['Genre','Year', 'Month', 'Day','Name','Hour','Minute','Second']\n",
        "cvlist = list(KFold(n_splits = 10, random_state = 1).split(train_df))\n",
        "for var in cat_vars:\n",
        "    mean_enc_var = np.zeros(len(train_df))\n",
        "    for tr_idx, val_idx in cvlist:\n",
        "        X_tr, X_val = train_df.loc[tr_idx], train_df.loc[val_idx]\n",
        "        X_tr_mean = mean_likelihood(X_tr, var, 'Views')\n",
        "        mean_enc_var[val_idx] = X_val[var].map(X_tr_mean['enc'])\n",
        "        train_df[f'mean_enc_{var}'] = mean_enc_var\n",
        "    train_df[f'mean_enc_{var}'] = train_df[f'mean_enc_{var}'].fillna(train_df[f'mean_enc_{var}'].mean())\n",
        "    test_df[f'mean_enc_{var}'] = test_df[var].map(mean_likelihood(train_df, \n",
        "                                                                    var, 'Views')['enc'])\n",
        "    test_df[f'mean_enc_{var}'] = test_df[f'mean_enc_{var}'].fillna(train_df[f'mean_enc_{var}'].mean())\n",
        "\n",
        "train_df.drop([\"Views\"], axis=1, inplace=True)\n",
        "alldata = pd.concat([train_df, test_df], axis=0, sort=False, ignore_index=True)'''"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A-yicFDQ-MW7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d59856d2-20fe-4fd2-cd7e-22114521105f"
      },
      "source": [
        "# Find non-numeric columns\n",
        "alldata.dtypes[alldata.dtypes == \"object\"].index"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Name', 'Genre', 'Country'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZnREfeS6-djV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "e3c55c64-d6e5-41b4-ef8f-14361468a5b6"
      },
      "source": [
        "print('Unique countries:', alldata['Country'].nunique())\n",
        "print('Unique genres:', alldata['Genre'].nunique())\n",
        "print('Unique names:', alldata['Name'].nunique())"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Unique countries: 1\n",
            "Unique genres: 21\n",
            "Unique names: 1160\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V3h2SBuh-rhF",
        "colab_type": "text"
      },
      "source": [
        "**Note:** We will drop *Country* and dummy-encode *Genre*. We will label-encode *Name* instead of dummy-encoding to avoid the curse of dimensionality and the resulting induced data sparseness; not because there exists an inherent ordering in *Name*."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ocdtxOn_oIHX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "c62b406d-f716-459f-b1c9-6a357eecc765"
      },
      "source": [
        "# Create dummy/one-hot encodings. Note: The only difference being that dummies convert n values of a categorical to n-1 variables,\n",
        "# whereas one-hot encodings create n variables\n",
        "alldata = pd.get_dummies(alldata, columns=['Genre'], drop_first=True)\n",
        "\n",
        "lbl = LabelEncoder()\n",
        "alldata['Name'] = lbl.fit_transform(alldata['Name'])\n",
        "\n",
        "alldata.drop(['Country','Timestamp'], axis=1, inplace=True)\n",
        "alldata.head()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Comments</th>\n",
              "      <th>Likes</th>\n",
              "      <th>Popularity</th>\n",
              "      <th>Followers</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>dayofyear</th>\n",
              "      <th>weekofyear</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>Hour</th>\n",
              "      <th>Minute</th>\n",
              "      <th>Second</th>\n",
              "      <th>Collaborators</th>\n",
              "      <th>Likes_Followers</th>\n",
              "      <th>Likes_Popularity</th>\n",
              "      <th>Likes_Comments</th>\n",
              "      <th>Followers_Popularity</th>\n",
              "      <th>Followers_Comments</th>\n",
              "      <th>Followers_Likes</th>\n",
              "      <th>Popularity_Followers</th>\n",
              "      <th>Popularity_Comments</th>\n",
              "      <th>Popularity_Likes</th>\n",
              "      <th>Total_Metrics</th>\n",
              "      <th>Year_Name_Likes_sum</th>\n",
              "      <th>Year_Name_Followers_sum</th>\n",
              "      <th>Year_Name_Popularity_sum</th>\n",
              "      <th>Year_Name_Comments_sum</th>\n",
              "      <th>NameLikes_mean</th>\n",
              "      <th>NameLikes_median</th>\n",
              "      <th>NameLikes_sum</th>\n",
              "      <th>NameLikes_min</th>\n",
              "      <th>NameLikes_max</th>\n",
              "      <th>NameFollowers_mean</th>\n",
              "      <th>NameFollowers_median</th>\n",
              "      <th>NameFollowers_sum</th>\n",
              "      <th>NameFollowers_min</th>\n",
              "      <th>NameFollowers_max</th>\n",
              "      <th>NamePopularity_mean</th>\n",
              "      <th>NamePopularity_median</th>\n",
              "      <th>NamePopularity_sum</th>\n",
              "      <th>NamePopularity_min</th>\n",
              "      <th>NamePopularity_max</th>\n",
              "      <th>NameComments_mean</th>\n",
              "      <th>NameComments_median</th>\n",
              "      <th>NameComments_sum</th>\n",
              "      <th>NameComments_min</th>\n",
              "      <th>NameComments_max</th>\n",
              "      <th>mean_enc_Genre</th>\n",
              "      <th>mean_enc_Year</th>\n",
              "      <th>mean_enc_Month</th>\n",
              "      <th>mean_enc_Day</th>\n",
              "      <th>mean_enc_Name</th>\n",
              "      <th>mean_enc_Hour</th>\n",
              "      <th>mean_enc_Minute</th>\n",
              "      <th>mean_enc_Second</th>\n",
              "      <th>Genre_alternativerock</th>\n",
              "      <th>Genre_ambient</th>\n",
              "      <th>Genre_classical</th>\n",
              "      <th>Genre_country</th>\n",
              "      <th>Genre_danceedm</th>\n",
              "      <th>Genre_deephouse</th>\n",
              "      <th>Genre_disco</th>\n",
              "      <th>Genre_drumbass</th>\n",
              "      <th>Genre_dubstep</th>\n",
              "      <th>Genre_electronic</th>\n",
              "      <th>Genre_folksingersongwriter</th>\n",
              "      <th>Genre_hiphoprap</th>\n",
              "      <th>Genre_indie</th>\n",
              "      <th>Genre_latin</th>\n",
              "      <th>Genre_metal</th>\n",
              "      <th>Genre_pop</th>\n",
              "      <th>Genre_rbsoul</th>\n",
              "      <th>Genre_reggaeton</th>\n",
              "      <th>Genre_rock</th>\n",
              "      <th>Genre_trap</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>395</td>\n",
              "      <td>4</td>\n",
              "      <td>499</td>\n",
              "      <td>97</td>\n",
              "      <td>119563</td>\n",
              "      <td>4</td>\n",
              "      <td>89</td>\n",
              "      <td>13</td>\n",
              "      <td>2018</td>\n",
              "      <td>3</td>\n",
              "      <td>30</td>\n",
              "      <td>15</td>\n",
              "      <td>24</td>\n",
              "      <td>45</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00417</td>\n",
              "      <td>5.14433</td>\n",
              "      <td>124.75000</td>\n",
              "      <td>1232.60825</td>\n",
              "      <td>29890.75000</td>\n",
              "      <td>239.60521</td>\n",
              "      <td>0.00081</td>\n",
              "      <td>24.25000</td>\n",
              "      <td>0.19439</td>\n",
              "      <td>120163</td>\n",
              "      <td>139243</td>\n",
              "      <td>28455994</td>\n",
              "      <td>20566</td>\n",
              "      <td>1586</td>\n",
              "      <td>768.75879</td>\n",
              "      <td>521.00000</td>\n",
              "      <td>1093175</td>\n",
              "      <td>47</td>\n",
              "      <td>25400</td>\n",
              "      <td>119563.00000</td>\n",
              "      <td>119563.00000</td>\n",
              "      <td>170018586</td>\n",
              "      <td>119563</td>\n",
              "      <td>119563</td>\n",
              "      <td>134.36146</td>\n",
              "      <td>101.00000</td>\n",
              "      <td>191062</td>\n",
              "      <td>4</td>\n",
              "      <td>2207</td>\n",
              "      <td>8.73840</td>\n",
              "      <td>6.00000</td>\n",
              "      <td>12426</td>\n",
              "      <td>0</td>\n",
              "      <td>158</td>\n",
              "      <td>265181.27478</td>\n",
              "      <td>344930.95509</td>\n",
              "      <td>821604.15775</td>\n",
              "      <td>589832.44105</td>\n",
              "      <td>29456.96788</td>\n",
              "      <td>327456.41285</td>\n",
              "      <td>486722.41135</td>\n",
              "      <td>648468.47262</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>21</td>\n",
              "      <td>17</td>\n",
              "      <td>49</td>\n",
              "      <td>17</td>\n",
              "      <td>2141</td>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>25</td>\n",
              "      <td>2016</td>\n",
              "      <td>6</td>\n",
              "      <td>20</td>\n",
              "      <td>5</td>\n",
              "      <td>58</td>\n",
              "      <td>52</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.02289</td>\n",
              "      <td>2.88235</td>\n",
              "      <td>2.88235</td>\n",
              "      <td>125.94118</td>\n",
              "      <td>125.94118</td>\n",
              "      <td>43.69388</td>\n",
              "      <td>0.00794</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.34694</td>\n",
              "      <td>2224</td>\n",
              "      <td>7929</td>\n",
              "      <td>17128</td>\n",
              "      <td>787</td>\n",
              "      <td>220</td>\n",
              "      <td>486.86667</td>\n",
              "      <td>183.00000</td>\n",
              "      <td>14606</td>\n",
              "      <td>37</td>\n",
              "      <td>5479</td>\n",
              "      <td>2141.00000</td>\n",
              "      <td>2141.00000</td>\n",
              "      <td>64230</td>\n",
              "      <td>2141</td>\n",
              "      <td>2141</td>\n",
              "      <td>59.43333</td>\n",
              "      <td>33.00000</td>\n",
              "      <td>1783</td>\n",
              "      <td>11</td>\n",
              "      <td>215</td>\n",
              "      <td>13.23333</td>\n",
              "      <td>7.50000</td>\n",
              "      <td>397</td>\n",
              "      <td>1</td>\n",
              "      <td>56</td>\n",
              "      <td>265181.27478</td>\n",
              "      <td>736096.73028</td>\n",
              "      <td>627699.01313</td>\n",
              "      <td>423931.60229</td>\n",
              "      <td>27033.58791</td>\n",
              "      <td>1376434.75381</td>\n",
              "      <td>510749.92840</td>\n",
              "      <td>298022.57546</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>653</td>\n",
              "      <td>11</td>\n",
              "      <td>312</td>\n",
              "      <td>91</td>\n",
              "      <td>22248</td>\n",
              "      <td>4</td>\n",
              "      <td>128</td>\n",
              "      <td>19</td>\n",
              "      <td>2015</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>17</td>\n",
              "      <td>45</td>\n",
              "      <td>59</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.01402</td>\n",
              "      <td>3.42857</td>\n",
              "      <td>28.36364</td>\n",
              "      <td>244.48352</td>\n",
              "      <td>2022.54545</td>\n",
              "      <td>71.30769</td>\n",
              "      <td>0.00409</td>\n",
              "      <td>8.27273</td>\n",
              "      <td>0.29167</td>\n",
              "      <td>22662</td>\n",
              "      <td>14258</td>\n",
              "      <td>1112400</td>\n",
              "      <td>4188</td>\n",
              "      <td>719</td>\n",
              "      <td>445.46114</td>\n",
              "      <td>282.00000</td>\n",
              "      <td>85974</td>\n",
              "      <td>59</td>\n",
              "      <td>3467</td>\n",
              "      <td>22248.00000</td>\n",
              "      <td>22248.00000</td>\n",
              "      <td>4293864</td>\n",
              "      <td>22248</td>\n",
              "      <td>22248</td>\n",
              "      <td>129.01554</td>\n",
              "      <td>76.00000</td>\n",
              "      <td>24900</td>\n",
              "      <td>11</td>\n",
              "      <td>1125</td>\n",
              "      <td>20.69948</td>\n",
              "      <td>12.00000</td>\n",
              "      <td>3995</td>\n",
              "      <td>0</td>\n",
              "      <td>179</td>\n",
              "      <td>265181.27478</td>\n",
              "      <td>868298.79185</td>\n",
              "      <td>547888.02419</td>\n",
              "      <td>523470.54702</td>\n",
              "      <td>19588.32840</td>\n",
              "      <td>402834.28143</td>\n",
              "      <td>561156.91817</td>\n",
              "      <td>613892.87075</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>304</td>\n",
              "      <td>2</td>\n",
              "      <td>2400</td>\n",
              "      <td>76</td>\n",
              "      <td>393655</td>\n",
              "      <td>3</td>\n",
              "      <td>159</td>\n",
              "      <td>23</td>\n",
              "      <td>2017</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>23</td>\n",
              "      <td>50</td>\n",
              "      <td>3</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00610</td>\n",
              "      <td>31.57895</td>\n",
              "      <td>1200.00000</td>\n",
              "      <td>5179.67105</td>\n",
              "      <td>196827.50000</td>\n",
              "      <td>164.02292</td>\n",
              "      <td>0.00019</td>\n",
              "      <td>38.00000</td>\n",
              "      <td>0.03167</td>\n",
              "      <td>396133</td>\n",
              "      <td>4234279</td>\n",
              "      <td>233437415</td>\n",
              "      <td>105357</td>\n",
              "      <td>4364</td>\n",
              "      <td>4090.31483</td>\n",
              "      <td>919.00000</td>\n",
              "      <td>7145780</td>\n",
              "      <td>0</td>\n",
              "      <td>159000</td>\n",
              "      <td>393655.00000</td>\n",
              "      <td>393655.00000</td>\n",
              "      <td>687715285</td>\n",
              "      <td>393655</td>\n",
              "      <td>393655</td>\n",
              "      <td>96.61477</td>\n",
              "      <td>34.00000</td>\n",
              "      <td>168786</td>\n",
              "      <td>0</td>\n",
              "      <td>4133</td>\n",
              "      <td>4.18546</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>7312</td>\n",
              "      <td>0</td>\n",
              "      <td>181</td>\n",
              "      <td>604235.43387</td>\n",
              "      <td>603845.28618</td>\n",
              "      <td>627699.01313</td>\n",
              "      <td>523470.54702</td>\n",
              "      <td>220774.83549</td>\n",
              "      <td>747189.79867</td>\n",
              "      <td>491572.36378</td>\n",
              "      <td>484799.93283</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1051</td>\n",
              "      <td>81</td>\n",
              "      <td>3031</td>\n",
              "      <td>699</td>\n",
              "      <td>201030</td>\n",
              "      <td>5</td>\n",
              "      <td>261</td>\n",
              "      <td>37</td>\n",
              "      <td>2016</td>\n",
              "      <td>9</td>\n",
              "      <td>17</td>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
              "      <td>19</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.01508</td>\n",
              "      <td>4.33619</td>\n",
              "      <td>37.41975</td>\n",
              "      <td>287.59657</td>\n",
              "      <td>2481.85185</td>\n",
              "      <td>66.32465</td>\n",
              "      <td>0.00348</td>\n",
              "      <td>8.62963</td>\n",
              "      <td>0.23062</td>\n",
              "      <td>204841</td>\n",
              "      <td>109640</td>\n",
              "      <td>5628840</td>\n",
              "      <td>23464</td>\n",
              "      <td>2759</td>\n",
              "      <td>2314.54678</td>\n",
              "      <td>974.50000</td>\n",
              "      <td>791575</td>\n",
              "      <td>0</td>\n",
              "      <td>64700</td>\n",
              "      <td>201030.00000</td>\n",
              "      <td>201030.00000</td>\n",
              "      <td>68752260</td>\n",
              "      <td>201030</td>\n",
              "      <td>201030</td>\n",
              "      <td>565.90936</td>\n",
              "      <td>244.00000</td>\n",
              "      <td>193541</td>\n",
              "      <td>0</td>\n",
              "      <td>15000</td>\n",
              "      <td>58.87719</td>\n",
              "      <td>33.50000</td>\n",
              "      <td>20136</td>\n",
              "      <td>0</td>\n",
              "      <td>1296</td>\n",
              "      <td>265181.27478</td>\n",
              "      <td>736096.73028</td>\n",
              "      <td>490372.93580</td>\n",
              "      <td>649635.43884</td>\n",
              "      <td>113185.64737</td>\n",
              "      <td>526598.82549</td>\n",
              "      <td>491572.36378</td>\n",
              "      <td>556654.62294</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Name  Comments  Likes  Popularity  Followers  dayofweek  dayofyear  \\\n",
              "0   395         4    499          97     119563          4         89   \n",
              "1    21        17     49          17       2141          0        172   \n",
              "2   653        11    312          91      22248          4        128   \n",
              "3   304         2   2400          76     393655          3        159   \n",
              "4  1051        81   3031         699     201030          5        261   \n",
              "\n",
              "   weekofyear  Year  Month  Day  Hour  Minute  Second  Collaborators  \\\n",
              "0          13  2018      3   30    15      24      45        0.00000   \n",
              "1          25  2016      6   20     5      58      52        0.00000   \n",
              "2          19  2015      5    8    17      45      59        0.00000   \n",
              "3          23  2017      6    8    23      50       3        0.00000   \n",
              "4          37  2016      9   17    20      50      19        1.00000   \n",
              "\n",
              "   Likes_Followers  Likes_Popularity  Likes_Comments  Followers_Popularity  \\\n",
              "0          0.00417           5.14433       124.75000            1232.60825   \n",
              "1          0.02289           2.88235         2.88235             125.94118   \n",
              "2          0.01402           3.42857        28.36364             244.48352   \n",
              "3          0.00610          31.57895      1200.00000            5179.67105   \n",
              "4          0.01508           4.33619        37.41975             287.59657   \n",
              "\n",
              "   Followers_Comments  Followers_Likes  Popularity_Followers  \\\n",
              "0         29890.75000        239.60521               0.00081   \n",
              "1           125.94118         43.69388               0.00794   \n",
              "2          2022.54545         71.30769               0.00409   \n",
              "3        196827.50000        164.02292               0.00019   \n",
              "4          2481.85185         66.32465               0.00348   \n",
              "\n",
              "   Popularity_Comments  Popularity_Likes  Total_Metrics  Year_Name_Likes_sum  \\\n",
              "0             24.25000           0.19439         120163               139243   \n",
              "1              1.00000           0.34694           2224                 7929   \n",
              "2              8.27273           0.29167          22662                14258   \n",
              "3             38.00000           0.03167         396133              4234279   \n",
              "4              8.62963           0.23062         204841               109640   \n",
              "\n",
              "   Year_Name_Followers_sum  Year_Name_Popularity_sum  Year_Name_Comments_sum  \\\n",
              "0                 28455994                     20566                    1586   \n",
              "1                    17128                       787                     220   \n",
              "2                  1112400                      4188                     719   \n",
              "3                233437415                    105357                    4364   \n",
              "4                  5628840                     23464                    2759   \n",
              "\n",
              "   NameLikes_mean  NameLikes_median  NameLikes_sum  NameLikes_min  \\\n",
              "0       768.75879         521.00000        1093175             47   \n",
              "1       486.86667         183.00000          14606             37   \n",
              "2       445.46114         282.00000          85974             59   \n",
              "3      4090.31483         919.00000        7145780              0   \n",
              "4      2314.54678         974.50000         791575              0   \n",
              "\n",
              "   NameLikes_max  NameFollowers_mean  NameFollowers_median  NameFollowers_sum  \\\n",
              "0          25400        119563.00000          119563.00000          170018586   \n",
              "1           5479          2141.00000            2141.00000              64230   \n",
              "2           3467         22248.00000           22248.00000            4293864   \n",
              "3         159000        393655.00000          393655.00000          687715285   \n",
              "4          64700        201030.00000          201030.00000           68752260   \n",
              "\n",
              "   NameFollowers_min  NameFollowers_max  NamePopularity_mean  \\\n",
              "0             119563             119563            134.36146   \n",
              "1               2141               2141             59.43333   \n",
              "2              22248              22248            129.01554   \n",
              "3             393655             393655             96.61477   \n",
              "4             201030             201030            565.90936   \n",
              "\n",
              "   NamePopularity_median  NamePopularity_sum  NamePopularity_min  \\\n",
              "0              101.00000              191062                   4   \n",
              "1               33.00000                1783                  11   \n",
              "2               76.00000               24900                  11   \n",
              "3               34.00000              168786                   0   \n",
              "4              244.00000              193541                   0   \n",
              "\n",
              "   NamePopularity_max  NameComments_mean  NameComments_median  \\\n",
              "0                2207            8.73840              6.00000   \n",
              "1                 215           13.23333              7.50000   \n",
              "2                1125           20.69948             12.00000   \n",
              "3                4133            4.18546              1.00000   \n",
              "4               15000           58.87719             33.50000   \n",
              "\n",
              "   NameComments_sum  NameComments_min  NameComments_max  mean_enc_Genre  \\\n",
              "0             12426                 0               158    265181.27478   \n",
              "1               397                 1                56    265181.27478   \n",
              "2              3995                 0               179    265181.27478   \n",
              "3              7312                 0               181    604235.43387   \n",
              "4             20136                 0              1296    265181.27478   \n",
              "\n",
              "   mean_enc_Year  mean_enc_Month  mean_enc_Day  mean_enc_Name  mean_enc_Hour  \\\n",
              "0   344930.95509    821604.15775  589832.44105    29456.96788   327456.41285   \n",
              "1   736096.73028    627699.01313  423931.60229    27033.58791  1376434.75381   \n",
              "2   868298.79185    547888.02419  523470.54702    19588.32840   402834.28143   \n",
              "3   603845.28618    627699.01313  523470.54702   220774.83549   747189.79867   \n",
              "4   736096.73028    490372.93580  649635.43884   113185.64737   526598.82549   \n",
              "\n",
              "   mean_enc_Minute  mean_enc_Second  Genre_alternativerock  Genre_ambient  \\\n",
              "0     486722.41135     648468.47262                      0              0   \n",
              "1     510749.92840     298022.57546                      0              0   \n",
              "2     561156.91817     613892.87075                      0              0   \n",
              "3     491572.36378     484799.93283                      0              0   \n",
              "4     491572.36378     556654.62294                      0              0   \n",
              "\n",
              "   Genre_classical  Genre_country  Genre_danceedm  Genre_deephouse  \\\n",
              "0                0              0               1                0   \n",
              "1                0              0               1                0   \n",
              "2                0              0               1                0   \n",
              "3                0              0               0                0   \n",
              "4                0              0               1                0   \n",
              "\n",
              "   Genre_disco  Genre_drumbass  Genre_dubstep  Genre_electronic  \\\n",
              "0            0               0              0                 0   \n",
              "1            0               0              0                 0   \n",
              "2            0               0              0                 0   \n",
              "3            0               0              0                 0   \n",
              "4            0               0              0                 0   \n",
              "\n",
              "   Genre_folksingersongwriter  Genre_hiphoprap  Genre_indie  Genre_latin  \\\n",
              "0                           0                0            0            0   \n",
              "1                           0                0            0            0   \n",
              "2                           0                0            0            0   \n",
              "3                           0                0            0            0   \n",
              "4                           0                0            0            0   \n",
              "\n",
              "   Genre_metal  Genre_pop  Genre_rbsoul  Genre_reggaeton  Genre_rock  \\\n",
              "0            0          0             0                0           0   \n",
              "1            0          0             0                0           0   \n",
              "2            0          0             0                0           0   \n",
              "3            0          0             1                0           0   \n",
              "4            0          0             0                0           0   \n",
              "\n",
              "   Genre_trap  \n",
              "0           0  \n",
              "1           0  \n",
              "2           0  \n",
              "3           0  \n",
              "4           0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VaR_4cs1oILC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b2c6bf5f-8562-4492-abb5-ffc6f07ab042"
      },
      "source": [
        "# Be mindful of null and inf values and convert them to the np.nan type\n",
        "# Maybe Pandas 1.0 will make life easier. See https://analyticsindiamag.com/whats-new-in-pandas-1-0/\n",
        "np.any(alldata.isnull())"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6aiKTtaGoINB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d5988815-e0d5-4068-9c8b-ec67cbe1e3d1"
      },
      "source": [
        "# Not going to use numpy.nan_to_num since infinity is replaced by very large finite values, potentially biasing our models\n",
        "alldata = alldata.replace([np.inf, -np.inf], np.nan)\n",
        "alldata = alldata.fillna(0)\n",
        "np.any(alldata.isna())"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C6VvnPXQSriw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Worse performance is observed after performing these transformations. Hence, omitted.\n",
        "'''from sklearn.preprocessing import StandardScaler, RobustScaler\n",
        "\n",
        "numeric_feats = alldata.dtypes[alldata.dtypes != \"object\"].index\n",
        "\n",
        "# Check the skew of all numerical features\n",
        "skewed_feats = alldata[numeric_feats].apply(lambda x: skew(x.dropna())).sort_values(ascending=False)\n",
        "print(\"\\nSkew in numerical features: \\n\")\n",
        "skewness = pd.DataFrame({'Skew' :skewed_feats})\n",
        "\n",
        "skewness = skewness[abs(skewness) > 0.75]\n",
        "print(\"There are {} skewed numerical features to Box Cox transform\".format(skewness.shape[0]))\n",
        "skewed_features = skewness.index\n",
        "for feat in skewed_features:\n",
        "    alldata[feat] = np.log1p(alldata[feat])\n",
        "\n",
        "sc = StandardScaler()\n",
        "alldata[numeric_feats] = sc.fit_transform(np.nan_to_num(alldata[numeric_feats]))'''"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aU9bf--4GTeF",
        "colab_type": "text"
      },
      "source": [
        "### Creating Train/Dev Splits"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sZs2Tj4RoIUt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ntrain = train_ID.shape[0] # We saved the 'Unique_ID' column at the beginning\n",
        "train = alldata[:ntrain]\n",
        "test = alldata[ntrain:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HOvYO8MKoIW7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d422127c-55f2-4585-f950-05c52ab82551"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_dev, y_train, y_dev = train_test_split(train, Y_train, test_size=0.25, random_state=0)\n",
        "X_train.shape, X_dev.shape, y_train.shape, y_dev.shape"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((58843, 77), (19615, 77), (58843,), (19615,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gvvMvZ41HmM_",
        "colab_type": "text"
      },
      "source": [
        "### Tree-Based Regression Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dB35xHLGoIkT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import ElasticNet, Lasso,  BayesianRidge, LassoLarsIC\n",
        "from sklearn.ensemble import RandomForestRegressor,  GradientBoostingRegressor\n",
        "from sklearn.kernel_ridge import KernelRidge\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\n",
        "from sklearn.model_selection import KFold, cross_val_score, train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from math import sqrt\n",
        "import xgboost as xgb\n",
        "import lightgbm as lgb"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bV-uuUhPoJCB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Validation function\n",
        "n_folds = 5\n",
        "\n",
        "def rmsle_cv(model):\n",
        "    kf = KFold(n_folds, shuffle=True, random_state=42).get_n_splits(train.values)\n",
        "    rmse= np.sqrt(-cross_val_score(model, train.values, Y_train, scoring=\"neg_mean_squared_error\", cv = kf))\n",
        "    return(rmse)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ONvbGZ2wbpB9",
        "colab_type": "text"
      },
      "source": [
        "#### LGBM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iizVEMjFoImp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "4a249883-ae6f-4c13-f954-f3749e30cab8"
      },
      "source": [
        "params = {\n",
        "    'boosting_type': 'gbdt',\n",
        "    'objective': 'regression',\n",
        "    'metric': 'l2_root',\n",
        "    'max_depth': -1,\n",
        "    'learning_rate': 0.1,\n",
        "    'feature_fraction': 0.8,\n",
        "    'verbose': 0,\n",
        "    'n_jobs': -1,\n",
        "    }\n",
        "n_estimators = 4000\n",
        "\n",
        "n_iters = 5\n",
        "preds_buf = []\n",
        "err_buf = []\n",
        "evals_result = {}\n",
        "for i in range(n_iters): \n",
        "    x_train, x_valid, y_train, y_valid = train_test_split(train, Y_train, test_size=0.25, random_state=i)\n",
        "    d_train = lgb.Dataset(x_train, label=y_train)\n",
        "    d_valid = lgb.Dataset(x_valid, label=y_valid)\n",
        "    watchlist = [d_valid]\n",
        "    \n",
        "    model = lgb.train(params, d_train, n_estimators, watchlist, verbose_eval=1000, early_stopping_rounds=200, evals_result=evals_result)\n",
        "    preds = model.predict(x_valid, num_iteration=model.best_iteration)\n",
        "    err = sqrt(mean_squared_error(y_valid, preds))\n",
        "    err_buf.append(err)\n",
        "    print('RMSLE = ' + str(err))\n",
        "\n",
        "\n",
        "print('Mean RMSLE = ' + str(np.mean(err_buf)) + ' +/- ' + str(np.std(err_buf)))"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training until validation scores don't improve for 200 rounds.\n",
            "Early stopping, best iteration is:\n",
            "[178]\tvalid_0's rmse: 620300\n",
            "RMSLE = 620300.2196834154\n",
            "Training until validation scores don't improve for 200 rounds.\n",
            "[1000]\tvalid_0's rmse: 906019\n",
            "Early stopping, best iteration is:\n",
            "[1269]\tvalid_0's rmse: 905552\n",
            "RMSLE = 905552.0867835734\n",
            "Training until validation scores don't improve for 200 rounds.\n",
            "[1000]\tvalid_0's rmse: 565667\n",
            "Early stopping, best iteration is:\n",
            "[1189]\tvalid_0's rmse: 565082\n",
            "RMSLE = 565081.7332982763\n",
            "Training until validation scores don't improve for 200 rounds.\n",
            "Early stopping, best iteration is:\n",
            "[543]\tvalid_0's rmse: 958234\n",
            "RMSLE = 958234.1119800737\n",
            "Training until validation scores don't improve for 200 rounds.\n",
            "Early stopping, best iteration is:\n",
            "[48]\tvalid_0's rmse: 941817\n",
            "RMSLE = 941817.0260835942\n",
            "Mean RMSLE = 798197.0355657866 +/- 169560.42921501474\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N0nJksswoIru",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "outputId": "7e8350e2-b1df-4be0-f9ab-a6e2b0ee169b"
      },
      "source": [
        "feature_imp = pd.DataFrame(sorted(zip(model.feature_importance(), alldata.columns), reverse=True)[:50], \n",
        "                           columns=['Value','Feature'])\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.barplot(x=\"Value\", y=\"Feature\", data=feature_imp.sort_values(by=\"Value\", ascending=False))\n",
        "plt.title('LGBM Feature Importances')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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N0b9/f86cOUNOTg49e/aka9euwPUZvK5du7Jnzx4mTJjA+++/z8iRI/nss8+4du0aQUFB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vvus6i1+b2ovFcEDx8+zLPPPgtA69atadq0Kenp6RWOkZaWRkxMDAB+fn689dZbAHh5eXHw4EGy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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EdQWBpd4byfM",
        "colab_type": "text"
      },
      "source": [
        "#### XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vdWwbqFZoIvA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "57c7ee79-3fb2-42cd-9c22-5072ed10116e"
      },
      "source": [
        "model_xgb = xgb.XGBRegressor(learning_rate=0.1,\n",
        "                          n_estimators=4000,\n",
        "                          verbosity=3,\n",
        "                          objective='reg:squarederror',\n",
        "                          n_jobs=-1,\n",
        "                          subsample=0.8,\n",
        "                          random_state=0,\n",
        "                          tree_method='gpu_hist')\n",
        "model_xgb.fit(X_train, y_train)\n",
        "y_pred_xgb = model_xgb.predict(X_dev)\n",
        "print('RMSE', sqrt(mean_squared_error(y_dev, y_pred_xgb)))"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE 3571726.820830554\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QxUnDkT5oI0s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "outputId": "4ddf1f0e-d336-41d3-9a16-848cd6cc669a"
      },
      "source": [
        "feature_imp = pd.DataFrame(sorted(zip(model_xgb.feature_importances_, alldata.columns), reverse=True)[:50], \n",
        "                           columns=['Value','Feature'])\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.barplot(x=\"Value\", y=\"Feature\", data=feature_imp.sort_values(by=\"Value\", ascending=False))\n",
        "plt.title('XGB Feature Importances')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FxMQEX19fTVt+fj4BAQHUqFGDSZMmcezYMUJDQ8nJyaFu3boEBwdTqVIlQkJC\nOHjwILq6ujg7O/PRRx+9cLxGjRqhp6dHamoqWVlZBAQEkJqaioWFBcHBwdSuXRt/f38MDAw4f/48\nGRkZ+Pv789ZbbxEREcH58+eZOXMmAGPGjGHkyJE4OTkVGGPcuHHcuXOH7Oxshg4dysCBA4HHM3gD\nBw7k+PHjmtUGp02bxvfff8+jR4/w9PSkcePG1KtXjypVqjB8+HAAFi1ahIWFBcOGDSuhrAshhBBC\nCFE6pMDSMpVKxZQpU2jSpAl+fn6kpKQQFhbG2rVrMTExYeXKlaxdu5YhQ4awf/9+YmJiUCgUPHz4\nsEj9nzt3DoVCgYWFBX5+fvTp04c+ffoQHh7O3LlzWb58OQBJSUmEh4dz69Ythg4dyptvvlnkZwgK\nCqJq1ao8evQIb29v3n77bczNzcnMzMTOzg5/f/8C10+ZMoXNmzcTGRkJQGJiIu+//z7Dhw8nPz+f\n6OhovvvuuyKPL4QQQgghRFklBZaWzZw5kx49euDn5wc8LoiuXbumeZUwNzeX1q1bU7lyZQwNDQkI\nCOCtt96iS5cuz+133bp17Nq1i0qVKrF48WIUCgXx8fGEhoYC4Onpyfz58zXX9+jRAx0dHerXr0/d\nunWfuW9VYTZu3Mj+/fsBuH37Njdv3sTc3BxdXV26d+/+wvutrKyoWrUqFy5cIDk5mebNm2Nubl7k\n8YUQQgghhCirpMDSMnt7e5RKJSNHjsTQ0BC1Wk2HDh1YuHDhU9eGh4dz4sQJYmJi2LRpExs2bHhm\nv09+g1VUCoXiqWNdXV3y8/M1bdnZ2U/dp1QqOX78OFu3bsXY2BgfHx/NdYaGhujq6hZp/P79+xMR\nEUFycjL9+vUrctxCCCGEEEKUZVJgaZm3tzdnzpxh4sSJLF26lNatWzN79mxu3rzJ66+/TmZmJnfv\n3sXS0pJHjx7RuXNn2rRpg6ura7HHsre3Jzo6Gi8vL6KionBwcNCci4mJoU+fPiQmJvL777/ToEED\n0tPT2bJlC/n5+dy9e5eff/75qT7T0tKoUqUKxsbGXL9+nbNnzxYpFj09PXJzc9HX1wfA1dWVJUuW\nkJeXx4IFC154v1qd/79lnYUQFYEq5+k/4AghhBAVgRRYJSgrK4tOnTppjkeMGFHodSNGjCAtLY1p\n06YREhJCcHAwH374ITk5j/eE+eCDD6hUqRLjxo3TzA7983dNRTFjxgymT5/O6tWrNYtcPFGrVi28\nvb3JyMggMDAQQ0ND2rZtS506dXB3d6dRo0a0aNHiqT47derEt99+S48ePWjQoAGtW7cuUiwDBgyg\nd+/eNG/enAULFmBgYICTk7SUTyoAACAASURBVBNmZmZFmvVSqxUkJ6cV/eHFv1JRdlYv6yTPQggh\nRMWlUKvV6tIOQmiXv78/Xbp0wc3NrdRiyM/Pp0+fPixZsoT69eu/8Hq1Oh+FQrZtE6K05OVkk/pX\n+doYWApZ7ZA8a4fkWTskz9pRUfJcvXrlQttlBkto3bVr1xgzZgzdunUrUnEFoFDocGmZ56sNTAjx\nTDbvRQLlq8ASQgghSoMUWK9YcnIywcHBnD17lipVqqCvr8+oUaPo1q1bsfsKCwsjJiamQJubm5tm\nRcKi+vzzz4s9touLC+Hh4VhYWBRo37JlC8bGxnh5eRW5r8aNG3PgwAESExOJiorCw8Oj2PEIIYQQ\nQghRFkmB9Qqp1Wree+89vLy8NAs5JCUlcfDgwX/Vn5+fn6aYUqlURV6x71V6srz8v5GUlMTu3bul\nwBJCCCGEEBWGFFiv0MmTJ9HX1y9QhNSpUwcfHx9UKhUhISGcOnWKnJwchgwZwqBBg1AqlSxduhRz\nc3OuXLlCixYtCAkJQaFQ4OLiQo8ePTh+/DijRo3C1taWwMBAUlNTMTIyYs6cOTRq1KjQWA4ePEhY\nWBi5ublUrVqVkJAQqlWrRmhoqGYlwdu3bzN9+nTOnj3L0aNHsbS05KuvvtKs/Ldq1SqOHj2KoaEh\nCxYs4PXXXyc0NBQTExN8fX25detWofH4+/tjamrK+fPnuX//PlOnTsXNzY0FCxZw/fp1PD096dOn\nD8OHD9fGxyKEEEIIIcQrI6sGvEJXr16lefPmhZ4LDw+ncuXKbN++ne3bt7Nt2zZ+//13AC5cuEBA\nQAB79uwhMTGRuLg4zX1Vq1Zlx44d9OzZkxkzZjBjxgwiIiL46KOPCAwMfGYsbdu2Zdu2bezcuZOe\nPXuyatUqzblbt26xfv16wsLCmDp1Kk5OTkRFRWFkZMSRI0c011WuXJmoqCjeffddgoKCnhrjefHc\nu3ePb775hhUrVmhm8yZPnoyDgwORkZFSXAkhhBBCiApBZrC0KDAwkLi4OPT19alTpw6XL1/m+++/\nBx7vL3Xz5k309fWxs7OjZs2aANjY2JCUlKTZw8rd3R2AjIwM4uPjmThxoqb/J8u8F+bOnTtMmjSJ\n+/fvk5OTg5WVleZcp06d0NfXx9raGpVKpVlq3tramsTERM11vXr1AqBnz54FlnwvSjyurq7o6OjQ\nuHFjkpOTi5E1IYQQQgghyg8psF6hJk2asG/fPs3xp59+SkpKCt7e3tSuXZtPPvmEjh07FrhHqVRi\nYGCgOdbV1UWlUmmOjY2Ngce/7zIzMyMyMrJIscydO5fhw4fTtWtXzWuITzwZT0dHB319fRQKheb4\n72M/z4vi+fszCSGEEEIIUVFJgfUKtW/fnoULF/LNN9/wzjvvAPDo0SMAnJ2d2bJlC+3bt0dfX58b\nN25Qo0aNIvdtamqKlZUVe/fupUePHqjVai5fvoyNjU2h16elpWn637lz5796nr179zJ69Gj27NmD\nvb39S8UDUKlSJTIyMoo0tlqd/79looUQpSEvJ7u0QxBCCCHKBSmwXiGFQsGyZcsIDg5m1apVWFhY\nYGxszJQpU3BzcyMpKYm+ffuiVqsxNzdn+fLlxep//vz5zJo1i7CwMPLy8nB3d39mQTN+/HgmTpxI\nlSpVcHJyKvDqX1H99ddfeHh4YGBgwMKFC18qHoCmTZuio6ND79696du373N/h6VWK0hOTit2zKJ4\nKsrGf2Wd5FkIIYSouBRqtVpd2kEI8SJqdT4KhazJIkRpycvJJvWv8rXRsBSy2iF51g7Js3ZInrWj\nouS5evXKhbbLDJZ4pqZNmzJixAj8/f0BWL16NZmZmbz//vtaj0Wh0OGnr2S/LCFKS5uxUUD5KrCE\nEEKI0iAFVgUTFhZGTExMgTY3NzfNBsXFYWBgwL59+xg9ejQWFhYlFaIQQgghhBAVlhRYFYyfn9+/\nKqYKo6enx8CBA1m/fj2TJk0qcO5lNy4+f/48n3/+OZmZmZibmxMcHIylpWWJxC2EEEIIIURpkR+1\niOcaMmQIUVFRpKUVXGDiZTYuzs3NZe7cuXz55ZdERETQr18/Fi1apO1HE0IIIYQQosTJDJZ4LlNT\nUzw9PdmwYQNGRkaa9pfZuPjGjRtcuXKFESNGAJCfn0/16tW1+2BCCCGEEEK8AlJgiRcaNmwYffv2\npW/fvpq2l9m4WK1W06RJE7Zu3ardBxFCCCGEEOIVk1cExQtVrVoVNzc3wsPDNW0vs3FxgwYNSElJ\nIT4+HoDc3FyuXr1acgELIYQQQghRSmQGSxTJyJEj2bx5s+b4ZTYuNjAw4Msvv2Tu3LmkpaWhUqkY\nNmwYTZo0eeY9anX+/5aJFkKUhryc7NIOQQghhCgXZKNhUS7k56t58CC9tMOo8CrKxn9lneRZOyTP\n2iF51g7Js3ZInrWjouRZNhoW5ZpCoX7ml1iULMmzdpR2nvNyskn9SzYOFkIIIUqaFFiiXFAodPhx\nZa/SDkOICqPD6N2AFFhCCCFESZNFLl6Svb39U21btmzRLPzg4+NDQkLCKxl7586d9OrVCw8PD7y8\nvFi9evUrGaekfPXVV6UdghBCCCGEEK+UzGC9AoMHD37lYxw5coT169ezevVqatSoQU5OTrFX89O2\nFStWMHbs2NIOQwghhBBCiFdGCqxXIDQ0FBMTE3x9fTVt+fn5BAQEUKNGDSZNmsSxY8cIDQ0lJyeH\nunXrEhwcTKVKlQgJCeHgwYPo6uri7OzMRx99VOgYK1euZNq0aZql0g0MDBgwYAAAFy9e5NNPPyUr\nK4t69eoRFBRElSpV8PHxoVmzZpw5c4asrCzmzZvHypUruXLlCj169GDSpEkkJiYyatQoWrduTXx8\nPC1btqRfv358+eWXpKSkEBISgp2dHZmZmcyZM4erV6+Sl5fH+PHjcXV1JSIigoMHD5KVlcXvv/+O\nq6sr06ZNIyQkhEePHuHp6Unjxo2ZM2cOH3zwAXfu3CE/P59x48bh7u7+6j8cIYQQQgghXiF5RVAL\nVCoVU6ZM4fXXX2fSpEmkpKQQFhbG2rVr2bFjBy1btmTt2rWkpqayf/9+oqOjiYqKws/P75l9Xr16\nlZYtWxZ6btq0aUyZMoWoqCisra0LbAKsr69PREQEgwYNYty4ccycOZPdu3ezY8cOUlNTAbh16xYj\nRoxg79693Lhxg6ioKLZs2cK0adM0r/l99dVXtG/fnvDwcDZs2MD8+fPJzHy8GszFixdZvHgxUVFR\n7N27l9u3bzNlyhSMjIyIjIxkwYIFHD16FEtLS3bt2sXu3bvp2LFjSaVbCCGEEEKIUiMFlhbMnDmT\nJk2aaAqmc+fOce3aNQYPHoynpyc7d+7kjz/+oHLlyhgaGhIQEMC+ffswMjIq9lhpaWmkpaXRrl07\nAPr06cOZM2c0511cXACwtramSZMmWFpaYmBgQN26dblz5w4AVlZWNG3aFB0dHRo3bswbb7yBQqGg\nadOmJCUlAXDs2DG+/vprPD098fHxITs7m9u3bwPwxhtvaJ6lUaNGmnv+ztramuPHjzN//nzOnDlD\n5cqycp0QQgghhCj/5BVBLbC3t0epVDJy5EgMDQ1Rq9V06NCBhQsXPnVteHg4J06cICYmhk2bNrFh\nw4ZC+2zcuDHnz5/njTfeKFYsBgYGAOjo6Gj+/eQ4Ly+vwDX/vE6hUKBSqTTnvvzySxo2bFig/3Pn\nzhW4X1dXt8A9TzRo0ICIiAiOHDnC4sWLad++PePHjy/WswghhBBCCFHWyAyWFnh7e9O5c2cmTpxI\nXl4erVu35qeffuLmzZsAZGZmcuPGDTIyMkhLS6Nz584EBARw+fLlZ/Y5ZswY5s+fz/379wHIycnh\nu+++o3LlypiZmWlmrSIjI3F0dCzxZ3J2dmbTpk082af6woULL7xHT0+P3NxcAO7evYuxsTGenp74\n+voW6X4hhBBCCCHKOpnBeklZWVl06tRJczxixIhCrxsxYgRpaWmaBR+Cg4P58MMPycl5vA/NBx98\nQKVKlRg3bhzZ2dkA+Pv7P3Pczp07k5yczIgRI1Cr1SgUCvr16wfAvHnzNItcPFlAo6SNGzeOoKAg\nevfuTX5+PlZWVqxYseK59wwYMIDevXvTvHlzvLy8+OKLL9DR0UFPT49Zs2Y99161Ov9/+/YIIUpC\nXk52aYcghBBCVEgK9ZMpCCHKsPx8NQ8epJd2GBVe1aom/PlnZmmHUeFJnrVD8qwdkmftkDxrh+RZ\nOypKnqtXL3wNAZnBEuWCQqF+5pdYlCzJs3Y8K8+5Odn8+VeOlqMRQgghREmp0AVWs2bNsLa2RqVS\n0bBhQ+bNm4exsXGJ9R8REcH58+eZOXNmke9JSEggMjKSTz75BKVSib6+Pm3atHnm9WFhYcTExBRo\nc3Nzw8/Pj9WrV/Pdd99haGiInp4ePj4+eHl5/evneZUePnxIVFQUQ4YM+Vf3KxQ6xK6SfbJExec6\nag8gBZYQQghRXlXoAuvJvksAkydP5ttvv33mb6S0IS8vD1tbW2xtbQE4deoUJiYmzy2w/Pz8Ct0P\na8uWLRw/fpzw8HBMTU1JT09n//79ryz2l/Xw4UO2bNnyrwssIYQQQgghyoP/zCqCDg4OmlX71q5d\nS69evejVqxfr1q0DIDExETc3NyZPnkyPHj2YMGECWVlZwOO9o1JSUoDHM1A+Pj5P9X/w4EH69++P\nl5cXw4cPJzk5GYDQ0FCmTp3KoEGDmDZtGkqlkjFjxpCYmMi3337LunXr8PT05MyZM7i4uGhW2UtP\nTy9w/E8rVqxg1qxZmJqaAmBqakqfPn0AOHHiBF5eXnh4eDB9+nTNQhouLi4sWLAAT09P+vbtyy+/\n/IKvry+urq5s2bIFAKVSybvvvoufnx9du3YlJCSEXbt24e3tjYeHB7du3QIgJSWF999/n379+tGv\nXz/i4uI0zzt9+nR8fHzo2rWrZpn5BQsWcOvWLTw9PZk3bx737t1jyJAheHp60qtXrwJ7dQkhhBBC\nCFFe/ScKrLy8PH744Qesra05f/48ERERbNu2ja1bt/Ldd99plgi/ceMG77zzDnv37qVSpUp88803\nRR6jbdu2bNu2jZ07d9KzZ09WrVqlOXf9+nXWrVtXYN8rKysrBg0axPDhw4mMjMTBwQEnJyeOHDkC\nQHR0NG+//Tb6+vpPjZWenk5GRgZ169Z96lx2djb+/v4sWrSIqKgoVCpVgeeoVauWZjx/f3+WLFnC\ntm3bCA0N1Vxz6dIlAgMD2bt3L5GRkfz222+Eh4fj7e3Nxo0bAfjss88YNmwY27dvJzQ0lE8++URz\n/40bNzSvLy5btozc3FwmT55MvXr1iIyM5KOPPmL37t04OzsTGRlJZGQkNjY2Rc61EEIIIYQQZVWF\nfkXw0aNHeHp6Ao9nsLy9vdmyZQuurq6YmJgA0K1bN83sUa1atWjbti0AvXv3ZuPGjfj6+hZprDt3\n7jBp0iTu379PTk4OVlZWmnMuLi4YGRm9sA9vb29WrVqFq6srERERzJkzp7iPzI0bN7CysqJBgwYA\n9OnTh82bNzN8+HAAunbtCoC1tTWZmZmaGTADAwMePnwIgK2tLZaWlgDUq1ePDh06aO5RKpUAHD9+\nnGvXrmnGfVL0weMl5A0MDLCwsMDCwoIHDx48FaetrS0BAQHk5eXh6upKs2bNiv2sQgghhBBClDUV\nusD6+2+wikKhUBR6rKurq9lQ98keVf80d+5chg8fTteuXVEqlSxdulRzrqgLa7Rt25bAwECUSiUq\nlQpra+tCrzM1NcXExITff/+90Fms53kyI6ajo4OBgYGmXUdHh7y8PICn2p8c6+jooFKpAMjPz2fb\ntm0YGho+Ncbf79fV1dX0+3eOjo5s2rSJI0eO4O/vz4gRI8rsAh1CCCGEEEIU1X/iFcG/c3BwIDY2\nlqysLDIzM4mNjcXBwQGAP/74g/j4eAB2796tmc2qU6cO58+fB2Dfvn2F9puWlkaNGjUA2LlzZ5Fi\nqVSpkmbW5wkvLy8mT55M3759n3vv6NGjCQwMJD398d5QGRkZ7Ny5kwYNGpCUlKT5vVlkZCSOjo5F\niqc4nJ2dNa8LAly8ePG51//zWZOSkqhWrRoDBgygf//+/PLLLyUeoxBCCCGEENpWoWewCtOiRQv6\n9u1L//79gcev5TVv3pzExEQaNGjA5s2bCQgIoHHjxgwePBiA8ePH8/HHH7NkyRKcnJwK7Xf8+PFM\nnDiRKlWq4OTkRGJi4gtjeeutt5gwYQIHDhxgxowZODg44OHhweLFi+nVq9dz733nnXfIzMykX79+\n6Ovro6enx4gRIzA0NCQ4OJiJEyeiUqlo2bKl5jlK0scff8zs2bPx8PBApVLh4ODA7Nmzn3m9ubk5\nbdq0oVevXnTs2BFra2tWr16Nnp4eJiYmzJs377njqdX5/1u+WoiKLTen8FlyIYQQQpQPCvWTd9/+\n4xITExk7diy7d+8u1ThiYmI4cOAA8+fPL9U4ypr8fDUPHqSXdhgVXkXZWb2skzxrh+RZOyTP2iF5\n1g7Js3ZUlDxXr1650Pb/3AxWWTZnzhx++OEHVq5cWdqhlDkKhfqZX2JRsiTP2vGsPOfmZPPnX7LR\nsBBCCFFeyQxWGRcYGMhPP/1UoG3o0KH069eP+/fvExQUREJCAmZmZrz22msEBARoVhAsS5RKJfr6\n+s/dVPlFdq/pUYIRCVE29Rq5l/v300o7jAqhovyFtKyTPGuH5Fk7JM/aUVHyLDNY5dSnn35aaLta\nrWb8+PF4eXmxaNEi4PH+VQ8ePCiTBdapU6cwMTF5qQJLCCGEEEKIsk4KrHLq5MmT6OnpFVjAwsbG\nBrVazbx58zh69CgKhQI/Pz/c3d1RKpWEhoZSuXJlrly5Qo8ePbC2tmbDhg1kZ2ezbNky6tWrh7+/\nP4aGhly8eJEHDx4QFBTEzp07OXv2LK1ateLzzz8H4NixY4SGhpKTk0PdunUJDg6mUqVKuLi44OXl\nxaFDh8jLy2Px4sUYGhry7bffoqOjw65du5gxYwb3799n2bJl6OjoULlyZTZv3lxaqRRCCCGEEKLE\nSIFVTl29epUWLVo81b5v3z4uXbpEZGQkqampeHt7a5ahv3TpEnv27KFq1ap07dqV/v37Ex4ezvr1\n69m4cSMff/wxAA8fPmTr1q0cOHAAPz8/tmzZQpMmTfD29ubixYvUqFGDsLAw1q5di4mJCStXrmTt\n2rWMHz8eeLxi4I4dO9i8eTNr1qzhs88+Y9CgQZiYmGg2bvbw8GD16tXUqFFDs8GxEEIIIYQQ5Z0U\nWBVMXFwcPXv2RFdXl2rVquHo6EhCQgKmpqbY2tpiaWkJQL169ejQoQMA1tbWKJVKTR9vvfUWCoWC\npk2bUq1aNZo2bQpA48aNSUpK4s6dO1y7dk0ze5abm0vr1q0197/99tsAtGzZkv379xcap729Pf7+\n/vTo0YNu3bqVfCKEEEIIIYQoBVJglVNNmjTh+++/L9Y9BgYGmn/r6OhojnV0dFCpVE9dp1Aonron\nLy8PHR0dOnTowMKFCwsdR19fv9B+/2727NmcO3eOw4cP069fP7Zv3465uXmxnkcIIYQQQoiyRqe0\nAxD/Tvv27cnJyWHr1q2atkuXLmFmZsbevXtRqVSkpKRw5swZ7OzsSnTs1q1b89NPP3Hz5k0AMjMz\nuXHjxnPvqVSpEhkZGZrjW7du0apVKyZOnIi5uTl37twp0RiFEEIIIYQoDTKDVU4pFAqWLl1KUFAQ\nX3/9NYaGhtSpU4eAgAAyMjLw9PREoVAwdepUqlevzq+//lpiY1tYWBAcHMyHH35ITs7j/Xo++OCD\n565e+NZbbzFhwgQOHDjAjBkzWLduHTdv3kStVtO+fXtsbGyeO6ZanU+vkXtL7BmEKKtyc7JLOwQh\nhBBCvATZB0uUC/n5ah48SC/tMCq8irIvRVknedYOybN2SJ61Q/KsHZJn7agoeZZ9sES5plCon/kl\nFiVL8qwd/8xzbk42f/6VU0rRCCGEEKKkSIFVATVr1gxra2vN8bJly7Cysir0WqVSyZo1a1ixYgUR\nERGcP3+emTNnaivUIlModAhf61baYQjxyniPiAGkwBJCCCHKOymwKiAjIyMiIyNLO4xnysvLQ09P\nvnpCCCGEEKLikVUE/yOys7OZPn06Hh4eeHl5cfLkyeden5iYyNChQ/Hw8GDYsGH88ccfqFQqXFxc\nUKvVPHz4kGbNmnH69GkAhgwZwm+//UZmZibTp0/H29sbLy8vYmNjAYiIiGDs2LEMHTqU4cOHc+/e\nPYYMGYKnpye9evXizJkzrzwHQgghhBBCvGoyjVABPXr0CE9PTwCsrKxYtmwZmzdvBiAqKorr16/j\n6+v73H205s6dS58+fejTpw/h4eHMnTuX5cuX06BBA65du0ZiYiLNmzfnzJkztGrVitu3b1O/fn0W\nLlxI+/btCQ4O5uHDh/Tv358333wTgAsXLrBr1y6qVq3KmjVrcHZ2xs/PD5VKRVZW1qtPjBBCCCGE\nEK+YFFgVUGGvCMbFxfHuu+8C0KhRI2rXrv3cvavi4+MJDQ0FwNPTk/nz5wPg4ODA6dOnSUxMZMyY\nMWzbtg1HR0dsbW0BOHbsGAcPHmTNmjXA45mz27dvA9ChQweqVq0KgK2tLQEBAeTl5eHq6kqzZs1K\nMANCCCGEEEKUDnlFUBSLo6MjcXFxJCQk0LlzZ9LS0jh16hQODg6aa7788ksiIyOJjIzk8OHDNGrU\nCABjY+MC/WzatIkaNWrg7+/Pzp07tf4sQgghhBBClDQpsP4jHBwciIqKAuDGjRvcvn2bhg0bPvN6\ne3t7oqOjgcevFT4poOzs7IiPj0ehUGBoaIiNjQ1bt27F0dERAGdnZzZt2sST7dUuXLhQaP9JSUlU\nq1aNAQMG0L9/f3755ZcSe1YhhBBCCCFKi7wi+B/xzjvvMGvWLDw8PNDV1SU4OBgDA4NnXj9jxgym\nT5/O6tWrsbCwIDg4GAADAwNq1qxJ69atgceFW3R0tGZZ+HHjxhEUFETv3r3Jz8/HysqKFStWPNX/\nqVOnWL16NXp6epiYmDBv3rznxq9W5/9vGWshKqbcnOzSDkEIIYQQJUChfjLVIEQZlp+v5sGD9NIO\no8KrKDurl3WSZ+2QPGuH5Fk7JM/aIXnWjoqS5+rVKxfaLjNYolxQKNTP/BKLkiV5Llm5uY/488/c\n0g5DCCGEEFoiBVYFFBoaiomJCb6+viXS3/Xr1/nwww9RKBR8+eWX1KtXr0T6LQ6FQodN67prfVwh\nXta7w78HpMASQggh/iukwBIvdODAAbp37864ceNe2RgqlQpdXd1X1r8QQgghhBDaIAVWBREWFsbO\nnTuxsLCgVq1atGjRgm3btrF161Zyc3N5/fXX+eKLL1CpVPTu3Zvvv/8efX190tPTNcfXrl3j008/\nJSsri3r16hEUFMTZs2dZv349Ojo6nDhxAgcHB6pUqcLw4cMBWLRoERYWFgwbNoxVq1axd+9ecnJy\n6NatGxMmTAAeL3xx584dsrOzGTp0KAMHDgQer1Q4cOBAjh8/zsyZMwss9S6EEEIIIUR5JMu0VwDn\nz59nz5497Ny5k6+//pqEhAQAunXrxvbt29m1axcNGzYkPDwcU1NTnJycOHLkCADR0dG8/fbb6Ovr\nM23aNKZMmUJUVBTW1tYsXbqUzp07M2jQIIYPH87GjRvp16+fZhPj/Px8oqOj6d27N8eOHePmzZuE\nh4cTGRnJL7/8wunTpwEICgoiIiKC7du3s3HjRlJTUwHIzMzEzs6OXbt2SXElhBBCCCEqBJnBqgDO\nnDmDq6urZiNfFxcXAK5evcrixYtJS0sjIyMDZ2dnALy9vVm1ahWurq5EREQwZ84c0tLSSEtLo127\ndgD06dOHiRMnPjWWlZUVVatW5cKFCyQnJ9O8eXPMzc358ccf+fHHH/Hy8gIeF0+//fYbjo6ObNy4\nkf379wNw+/Ztbt68ibm5Obq6unTvLr+rEkIIIYQQFYcUWBWYv78/y5cvx8bGhoiICE6dOgVA27Zt\nCQwMRKlUolKpsLa2Ji0trcj99u/fn4iICJKTk+nXrx8AarWa0aNHM2jQoALXKpVKjh8/ztatWzE2\nNsbHx4fs7Mf7/RgaGsrvroQQQgghRIUirwhWAI6OjsTGxvLo0SPS09M5dOgQABkZGVSvXp3c3Fyi\noqIK3OPl5cXkyZPp27cvAJUrV8bMzIwzZ84AEBkZiaOjY6Hjubq6cvToURISEjSzYs7Ozmzfvp2M\njAwA7t69y4MHD0hLS6NKlSoYGxtz/fp1zp49+0pyIIQQQgghRFkgM1gVQIsWLXB3d8fT0xMLCwts\nbW0BmDhxIv3798fCwoJWrVppih8ADw8PFi9eTK9evTRt8+bN0yxyUbduXYKDgwsdz8DAACcnJ8zM\nzDQzUM7Ozly/fl0zg2ViYsL8+fPp1KkT3377/9i786gqy67x498DgogTkmgpmhPgnCiGPZoaYilK\nTI6ZAuqraSqZQziHopiSGpgYD6I45CMhSkiSA0W1LEwiX8kRfyQetTTJPIDA4Zzz+4PH80aCgsJh\ncH/Wai3v6br3vWW13Fz3va//MHz4cNq3b0+vXr0e6xl1Ou1/210LUbuo1fnVHYIQQgghDEih0+l0\n1R2EMLzExESOHz/O+vXrK3ytVqvFw8ODjz76iHbt2lV+cKXeU8ft2zkGudfTrK6srF7TSZ4NQ/Js\nGJJnw5A8G4bk2TDqSp6trBqXul9msJ5Cq1at4ptvviE8PLzC12ZkZDB9+nSGDh1qsOIKQKHQlflD\nLCqX5PnxFarz+euOLCoshBBCPM1kBkvUGtt2SsdBUbNNmfQlt249umFMXfnNXU0neTYMybNhSJ4N\nQ/JsGHUlz2X9UlqaXFSQnZ0da9eu1W9v27aN0NDQKr2nv78/iYmJJfb9/vvv+oV8Y2NjWblyZZXG\nIIQQQgghhHg0KbAqLmDwNAAAIABJREFUyNTUlCNHjpCdnV2tcbRs2ZKQkJBqjUEIIYQQQghRknyD\nVUH16tVj7NixREVFMXfu3BLHkpKSCAsLQ61WY2FhQXBwMM2bNyc0NBSlUsnVq1e5ceMGixYt4uef\nf+bbb7+lRYsWbN26FRMTE9LT01m7di15eXk0a9aMoKAgWrRoUWocSqWSt956i0OHDpXY//XXXxMW\nFkZYWBgAK1as4Pr16wAsXryYPn36cPLkSVavXg2AQqFg9+7dNGrU6IF7pKSkEBoaSuPGjbl48SLD\nhw/H1taWnTt3UlBQwMcff0zbtm3Jzs4u9T7/+7//y+rVqykoKMDMzIw1a9bQoUMHYmNjSUpK4t69\ne1y9ehVnZ2cWLlz4ZH8xQgghhBBC1AAyg/UYJkyYQHx8/AOL8/bp04fo6GgOHjzIiBEjiIiI0B/L\nysoiKiqKsLAwFixYgKOjI/Hx8ZiZmZGcnIxarSYwMJCQkBBiY2Px8vJi48aNFYrr6NGjhIeHEx4e\njqWlJatXr8bb25v9+/cTGhrK0qVLAYiMjGT58uXExcWxZ88ezMzMyhzz/PnzBAQEcPjwYeLi4vj1\n11+JiYlh1KhR7Nq1C6DM+3To0IE9e/Zw8OBB5syZU+J5zp07x6ZNm4iPj+fw4cPcuHGjQs8qhBBC\nCCFETSQzWI+hUaNGuLm5sXPnzhLFyW+//cbcuXO5desWhYWFWFtb648NHDgQExMTbG1t0Wg0DBw4\nEABbW1uUSiWZmZlcvHgRX19foLgVupWVVblj+uGHH0hPTycyMlI/G3XixAkyMjL05+Tk5JCbm0vv\n3r1Zu3Ytrq6uvPrqqzRs2LDMcXv06KGfRWvbti39+/fXx52SkvLQ+6hUKt577z2uXLmCQqFArf6/\n7movvfQSjRsXfxjYsWNHrl27xnPPPVfu5xVCCCGEEKImkgLrMXl7e+Pp6Ymnp6d+X2BgID4+PgwZ\nMoSUlBQ2b96sP2ZqagqAkZERJiYmKBQK/bZGo0Gn02FjY8O+ffseK562bdty9epVMjMz9QsNa7Va\noqOjqV+/folzp02bxqBBg0hOTmb8+PFERETQsWPHUse9H/f9WP/+HBqN5qH3WbVqFY6Ojnz88cco\nlUomTZpU6rjGxsb6sYQQQgghhKjNpMB6TBYWFgwbNoyYmBi8vLwAUKlUtGzZEoCDBw9WaLz27duT\nnZ1NWloa9vb2qNVqfv31V2xsbMp1fatWrViwYAGzZ8/mo48+wsbGhgEDBrBr1y6mTp0KFL+W16VL\nF7KysrCzs8POzo709HQyMzPLLLDKo6z7/D0fBw4ceOzxAXQ6LVMmfflEYwhR1QrV+dUdghBCCCGq\nmRRYT2Dy5Mns2bNHvz1r1iz8/Pxo2rQpjo6OKJXKco9lampKSEgIgYGBqFQqNBoN3t7e+gJrxYoV\nrFmzBoDnnnuODz/88IExOnbsSHBwMH5+fmzdupUlS5awcuVKXF1d0Wg0ODg4sHLlSqKiokhJSUGh\nUGBjY6N/XfFxlXWfqVOn4u/vT1hYGIMGDXqie+h0Cv7449HrC4knU1fWpRBCCCGEqC6y0LCoFXQ6\nLQqF9GQRNUehOp+/7qgffWIppJA1DMmzYUieDUPybBiSZ8OoK3kua6FhmcGqA0JDQzE3N2fKlCmV\nMt7ly5d59913USgUhISE0LZt20oZ976JEyeycOFC/bdi5aFQGPHx7tcqNQ4hnsTbb34JPF6BJYQQ\nQoi6SwoswYULF0qsQ3X79m2MjIz45ptvqjEqIYQQQgghah9556qWCgsL47XXXmP8+PFkZmYCEB0d\njZeXF6+//jqzZ8/m3r175OTk4OTkpG+R/vftc+fOMWbMGObPn4+1tTU7d+7k3XffRafTodPpmDhx\nIhEREezcuROANWvW6DsBfv/998ybNw+A7777jrFjx+Lh4cGcOXPIzc0FID09nTfffBNPT0+mTJnC\nzZs3SzyDVqvF39+/wut9CSGEEEIIUVNJgVULpaen88UXX3Dw4EH+/e9/c+bMGQCGDh3K/v37+fzz\nz+nQoQMxMTE0atQIR0dHkpOTAUhISODVV1/FxMSEhQsXMn/+fOLj47G1tWXz5s0MGjSIcePG4ePj\nw65du3BwcODUqVP6++bl5aFWq0lNTaVv375kZ2cTFhbG9u3bOXDgAN27d2f79u2PXDhZo9Ewf/58\nnn/+eebOnWv4JAohhBBCCFEF5BXBWujUqVM4OzvToEEDAJycnAC4dOkSmzZtQqVSkZuby4ABAwAY\nNWoUERERODs7Exsby6pVq1CpVKhUKl588UUAPDw88PPze+Be3bp145dffiEnJwdTU1O6du1Keno6\np06dYunSpZw+fZqMjAzGjx8PgFqtplevXo9cOHn58uUMHz6cGTNmVF2ihBBCCCGEMDApsOoQf39/\ntmzZQufOnYmNjeXkyZMA9OnTh4CAAFJSUtBoNNja2qJSla/luYmJCdbW1sTGxmJvb4+dnR0pKSlk\nZWXRsWNHsrKy6N+/Pxs2bChx3YULFx66cLK9vT0pKSlMnjz5gQWKhRBCCCGEqK3kFcFaqG/fvhw7\ndoz8/HxycnL46quvAMjNzcXKygq1Wk18fHyJa9zd3Zk3bx6enp4ANG7cmCZNmuhf/4uLi6Nv376l\n3s/BwYHIyEj69u2Lg4MD//nPf+jSpQsKhYJevXrx008/ceXKFQDy8vLIzMwssXAyFM9sXbp0ST/m\nqFGjGDRoEH5+fhQVFVVugoQQQgghhKgmMoNVC3Xr1g0XFxfc3NywtLTUtzv38/Nj9OjRWFpa8sIL\nL+ibTQC4urqyadMmRo4cqd/3wQcfsGLFCu7du0ebNm0ICgoq9X4ODg5s3bqVXr16YW5uTv369XFw\ncADA0tKSoKAg3n33XQoLCwF45513aN++/UMXTgbw9fVFpVKxcOFCgoODMTIqu97X6bT/bYstRM1Q\nqM6v7hCEEEIIUQPJQsNPicTERI4fP8769eurO5THotXquH07p7rDqPPqysJ/NZ3k2TAkz4YheTYM\nybNhSJ4No67kWRYafoqtWrWKb775hvDw8OoO5fEpdGX+EIvKVdfzXKjO5687skCwEEIIIaqGFFhP\ngWXLllXZ2F26dMHW1paioiKMjY1xd3fHx8fnoa/7PQ4jhRHBe1+r1DHF02n++C8BKbCEEEIIUTWk\nwBJPxMzMjLi4OABu377NvHnzyMnJYc6cOdUcmRBCCCGEEIYnXQRFpXnmmWdYtWoVe/bsQafToVQq\neeONN/Dw8MDDw4OffvoJgIULF3Ls2DH9dfPmzSuxLYQQQgghRG0lBZaoVG3atEGj0XD79m2eeeYZ\ntm/fzoEDB9i4cSOBgYFAcYv22NhYAFQqFWlpaQwePLgaoxZCCCGEEKJyyCuCosoUFRWxcuVKzp8/\nj5GREb/++isAL774IgEBAWRnZ/Pll1/y2muvUa+e/CgKIYQQQojaT2awRKW6evUqxsbGPPPMM+zY\nsYPmzZsTFxfH/v37Uav/r7GAm5sbn3/+ObGxsXh5eVVjxEIIIYQQQlQeKbBEpcnOzmbFihVMmDAB\nhUKBSqXCysoKIyMj4uLi0Gg0+nM9PT2JiooCoFOnTtUVshBCCCGEEJVK3ssSTyQ/Px83Nzd9m3Y3\nNzd8fX0BeOONN5g9ezYHDx7k5ZdfxtzcXH9d8+bN6dChA87OztUVuhBCCCGEEJVOodPpdNUdhHj6\n3Lt3D1dXVw4cOEDjxo9e2Far02KkkAlX8eRqwkLDdWUF+5pO8mwYkmfDkDwbhuTZMOpKnq2sSv83\nrMxgCYM7ceIES5Yswdvbu1zFFQA6Bbf+UFVtYKLO/A9PCCGEEKK6SIElKsTe3p60tDT9dmxsLOnp\n6SxfvrzcY/zrX//iq6++qtiNFboyf0sgKlddznNNmL0SQgghRN0mBZaoEYqKih7aqt1IYcT70a8Z\nMCJRF70/5ktACiwhhBBCVB35qEVUGqVSyaRJk3B1dcXb25vr168D4O/vT2Jiov48e3t7AFJSUnjj\njTd46623GDFiRLXELIQQQgghRGWSGSxRIfe7Bt73119/4eTkBEBgYCAeHh54eHgQExNDYGAgW7Zs\neeh4Z8+eJT4+njZt2lRp3EIIIYQQQhiCFFiiQszMzIiLi9Nv3/8GCyAtLY3Q0FCgeCHh9evXP3K8\nHj16SHElhBBCCCHqDHlFUFQ5Y2NjtFotAFqtFrX6/76B+fvaWEIIIYQQQtR2UmCJSmNvb09CQgIA\n8fHxODg4ANC6dWt++eUXAJKSkkoUWEIIIYQQQtQlUmCJSrNs2TJiY2NxdXUlLi6OJUuWADBmzBh+\n/PFHXn/9ddLS0mTWSgghhBBC1FkKnU6nq+4ghHgUrU6LkUJ+HyCeTE1ZB0sWdDYMybNhSJ4NQ/Js\nGJJnw6greS5r7VBpciFqB52CW3+oqjuKOq+u/A9PCCGEEKK6SIElageFrszfEojKVZfzXKAu4O6d\nwuoOQwghhBB1mBRYolYwUhjxzv5h1R2GqOU2eSUCUmAJIYQQoupIgVUL/fHHHwQFBfHzzz/TtGlT\nTExMmDp1KkOHDjVoHMeOHaNdu3Z06tTJoPcVQgghhBCippKuAbWMTqfj7bffxsHBgePHjxMbG8uG\nDRv47bffnnhsjUZTofOPHTtGRkZGqceKioqeOB4hhBBCCCFqG5nBqmV++OEHTExMGD9+vH5f69at\nmThxIhqNhuDgYE6ePElhYSETJkxg3LhxpKSksHnzZpo1a8bFixfp1q0bwcHBKBQKnJycGD58OCdO\nnGDq1Kn06NGDgIAA/vzzT8zMzFi1ahUdO3Z8II6ffvqJpKQkTp48SVhYGKGhoSxZsoTOnTuTmprK\nyJEjadeuHWFhYajVaiwsLAgODqZ58+aEhoaSlZVFVlYWf/75J1OnTmXMmDGGTKMQQgghhBBVQgqs\nWubSpUt07dq11GMxMTE0btyY/fv3U1hYyLhx4+jfvz8AZ8+eJSEhgRYtWjB+/HhSU1P1CwFbWFhw\n4MABALy9vQkICKBdu3acPn2agIAAdu7c+cC9evfujZOTE4MHD2bYsP/7NkqtVhMbGwvAX3/9RXR0\nNAqFgs8++4yIiAj8/f0BuHDhAtHR0eTl5eHh4cGgQYNo2bJl5SVKCCGEEEKIaiAFVi0XEBBAamoq\nJiYmtG7dmgsXLvDll18CoFKpuHLlCiYmJvTs2ZNnn30WgM6dO3Pt2jV9geXi4gJAbm4uaWlp+Pn5\n6ccvLKxYQ4D7YwH89ttvzJ07l1u3blFYWIi1tbX+2JAhQzAzM8PMzAxHR0fOnDkjBZYQQgghhKj1\npMCqZWxsbDhy5Ih+e8WKFWRnZzNq1ChatWrF0qVLefnll0tck5KSgqmpqX7b2Ni4xPdWDRo0AIq/\n72rSpAlxcXGPHd/9sQACAwPx8fFhyJAh+tcU71MoFI99DyGEEEIIIWoqKbBqmX79+rFhwwY+/fRT\n3njjDQDy8/MBGDBgAHv37qVfv36YmJiQmZlZoVmhRo0aYW1tzeHDhxk+fDg6nY4LFy7QuXPnUs9v\n2LAhubm5ZY6nUqn09z948GCJY8ePH2f69Onk5eVx8uRJ5s2b99DYtDrtf1tsC/H4CtQF1R2CEEII\nIeo4KbBqGYVCwccff0xQUBARERFYWlrSoEED5s+fz7Bhw7h27Rqenp7odDqaNWvGli1bKjT++vXr\nef/99wkLC6OoqAgXF5cyCywXFxeWLVvGrl27CAkJeeD4rFmz8PPzo2nTpjg6OqJUKvXH7OzsmDRp\nEn/++SczZ858dCGoU3DrD1WFnkVUnIWFOXfu5FV3GEIIIYQQtZZCp9PpqjsI8XQJDQ3F3NycKVOm\nlPsarU6LkUJWFRAVl68uQHWnZi0uLIWsYUieDUPybBiSZ8OQPBtGXcmzlVXjUvfLDJYoF3t7e9LS\n0krs27t3Lw0aNMDd3Z2JEyeycOFCevToUSX3N1IYMSZu2KNPFOIfot0SUVGzCiwhhBBC1F1SYIlH\nCgsL4969e7i5uen3DRs2jBkzZjzWeLNnz66s0IQQQgghhKhRpMASjzRjxgzCw8Mf6C5Y2qt+Wq2W\nxYsX07JlS+bOnct3331HaGgohYWFtGnThqCgIBo2bEhwcDBJSUkYGxszYMAA3nvvPUM/lhBCCCGE\nEJVOCixRaTQaDfPnz8fGxoYZM2aQnZ1NWFgY27dvx9zcnPDwcLZv386ECRM4evQoiYmJKBQK7t69\nW92hCyGEEEIIUSmkwBKVZvny5QwfPlz/6uDp06fJyMhg/PjxAKjVanr16kXjxo2pX78+ixcv5pVX\nXmHw4MHVGLUQQgghhBCVRwosUWns7e1JSUlh8uTJ1K9fH51OR//+/dmwYcMD58bExPD999+TmJjI\n7t272blzZzVELIQQQgghROWSvtei0owaNYpBgwbh5+dHUVERvXr14qeffuLKlSsA5OXlkZmZSW5u\nLiqVikGDBrF48WIuXLhQzZELIYQQQghROWQGS5TLvXv3GDhwoH7b19e31PN8fX1RqVQsXLiQ4OBg\ngoKCePfddyksLG6T/c4779CwYUNmzpxJQUEBAP7+/o+8v1anJdotsRKeRDxt8tUF1R2CEEIIIZ4i\nstCwqBW0Wh23b+dUdxh1Xl1Z+K+mkzwbhuTZMCTPhiF5NgzJs2HUlTzLQsOidlPoyvwhFpWrruU5\nX12A6o4sNCyEEEIIw5ACS9QKRgojhsdNefSJQvzDYbdtqJACSwghhBCG8dQ0uejSpQtubm76/5RK\nZZnnpqSkMH36dABiY2NZuXKlocIst9zcXJYvX46zszOenp5MnDiR06dPV3dYZTp37hzJycnVHYYQ\nQgghhBBV6qmZwTIzMyMuLq66wyhTUVER9eqV/69j6dKlWFtbc+TIEYyMjLh69SqXL1+uwgifzLlz\n50hPT2fQoEHVHYoQQgghhBBV5qkpsEpTUFDA+++/T3p6OsbGxvj7+9OvX78yz1cqlSxevJg///wT\nS0tLgoKCaNmyJUOHDuX48eOoVCocHR3ZuXMnffv2ZcKECaxevZoWLVqwatUqLl26RFFREbNmzcLZ\n2ZnY2FiOHDlCXl4eWq2WDRs2MHfuXHJyctBoNLz//vs4ODg8EEdWVhanT58mODgYI6PiScg2bdrQ\npk0bALZv387+/fuB4tbpPj4+KJVKpk6dSq9evUhLS6N79+54eXkREhJCdnY2wcHB9OzZk9DQUJRK\nJVevXuXGjRssWrSIn3/+mW+//ZYWLVqwdetWTExMSE9PZ+3ateTl5dGsWTOCgoJo0aIFEydOpGfP\nnqSkpKBSqVi9ejU9e/YkJCSE/Px8UlNTmT59Os2bN2f16tUAKBQKdu/eTaNGjSr7r1gIIYQQQgiD\nempeEczPz9e/Hvj2228DsGfPHgDi4+P58MMP8ff317cOL01gYCAeHh7Ex8fj6upKYGAgxsbGtG/f\nnoyMDFJTU+natSunTp2isLCQGzdu0K5dO7Zu3Uq/fv2IiYlh586drF+/nry84s4pZ8+eJSQkhN27\nd3Po0CEGDBhAXFwccXFxdO7cudQ4Ll26RJcuXTA2Nn7gWHp6OrGxsURHR7Nv3z4+++wzzp49CxQX\nZr6+vhw+fJjMzEzi4+PZu3cvCxcuZOvWrfoxsrKyiIqKIiwsjAULFuDo6Eh8fDxmZmYkJyejVqsJ\nDAwkJCSE2NhYvLy82Lhxo/56jUZDTEwMixcvZvPmzZiamjJnzhxcXFyIi4vDxcWFyMhIli9fTlxc\nHHv27MHMzKyCf6NCCCGEEELUPE/NDFZprwimpqby5ptvAtCxY0datWpFZmZmmWOkpaURGhoKgJub\nG+vXrwfAwcGBH3/8EaVSyfTp04mOjqZv37706NEDgO+++46kpCQiIyOB4pmzGzduANC/f38sLCwA\n6NGjB4sXL6aoqAhnZ2e6dOlS4edMTU3F2dkZc3NzAIYOHcqpU6dwcnLC2toaOzs7ADp16sRLL72E\nQqHAzs6Oa9eu6ccYOHAgJiYm2NraotFo9Otf2draolQqyczM5OLFi/q1sLRaLVZWVvrrhw4dCkC3\nbt1KjPt3vXv3Zu3atbi6uvLqq6/SsGHDCj+rEEIIIYQQNc1TU2BVpb59+7J3715u3ryJn58f27Zt\n4+TJkyVe7wsJCaFDhw4lrjt9+jQNGjQoMc7u3btJTk7G398fX19f3N3dH7ifjY0N58+fR6PRlDqL\nVRZTU1P9n42MjPTbCoUCjUbzwHlGRkaYmJigUCj02xqNBp1Oh42NDfv27Xvofe6fX5pp06YxaNAg\nkpOTGT9+PBEREXTs2LHczyKEEEIIIURN9FQXWA4ODsTHx/PSSy+RmZnJjRs36NChA2lpaaWeb29v\nT0JCAu7u7sTHx+sLqJ49e7Jw4UKsra2pX78+nTt3Zt++fXzyyScADBgwgN27d7Ns2TIUCgVnz56l\na9euD4x/7do1nn32WcaMGUNhYSG//PJLqQVW27Zt6d69OyEhIbzzzjsoFAqUSiUZGRk4ODjg7+/P\ntGnT0Ol0HDt2jHXr1lVi1qB9+/ZkZ2eTlpaGvb09arWaX3/9FRsbmzKvadiwIbm5ufrtrKws7Ozs\nsLOzIz09nczMzIcWWFqdlsNu2yr1OcTTIV9d9mu/QgghhBCV7akusN544w3ef/99XF1dMTY2Jigo\nqMQszz8tW7aMRYsWsW3bNn2TCyiesXn22Wfp1asXUFy4JSQkYGtrC8DMmTNZs2YNr7/+OlqtFmtr\na33x9XcnT55k27Zt1KtXD3Nzcz744IMyY1m9ejVr165l6NChmJmZ0axZMxYsWEC3bt3w9PRk9OjR\nQHGTi65duz60LX1FmZqaEhISQmBgICqVCo1Gg7e390MLLEdHR8LDw3Fzc2P69OmkpqaSkpKCQqHA\nxsZG/xpimXQKbv2hqrRnEKWrKyurCyGEEEJUF4VOp9NVdxBCPIpWp8VI8dT0ZBEVlK8uRHWn9sxU\nSSFrGJJnw5A8G4bk2TAkz4ZRV/JsZdW41P1P9QyWqD2MFEa4HHyvusMQNdQX7h+govYUWEIIIYSo\nu2RKoIrY29s/sG/v3r0cPHgQgIkTJ3LmzJlHjjN69Gh9e/n7/124cKHM80NDQ3n55Zf15wYHBz90\n/L/H4eTkRHZ29iNjEkIIIYQQQpROZrAMaPz48RW+5rPPPqvwNT4+PkyZMqXC1xlKRbsfCiGEEEII\nUVvIDJYBhYaGsm1byU54Wq0Wf39//UK93333HWPHjsXDw4M5c+boO+8FBwfj4uKCq6vrQ5tflOX7\n77/H3d0dV1dXFi1aRGFh4UPP3759OyNHjmTkyJHs2LEDgIiICHbu3AnAmjVrmDRpkn7sefPmPTR+\nJycn1q9fj4eHB4mJiezcuVP/PHPnzq3w8wghhBBCCFETyQxWNdJoNMyfPx8bGxtmzJhBdnY2YWFh\nbN++HXNzc8LDw9m+fTsTJkzg6NGjJCYmolAouHv37kPH3bFjB59//jkA8+fP58UXX8Tf358dO3bQ\nvn17Fi5cyKeffoqPj0+p16enpxMbG0t0dDQ6nY4xY8bw4osv4uDgQGRkJJMmTSI9PZ3CwkLUajWp\nqan07du3zPhnzZoFgIWFBQcOHACKW9cnJSVhamr6yOcRQgghhBCitpACqxotX76c4cOHM2PGDKB4\n4eGMjAz9q4RqtZpevXrRuHFj6tevz+LFi3nllVcYPHjwQ8f95yuC58+fx9ramvbt2wPg4eHBnj17\nyiywUlNTcXZ2xtzcHIChQ4dy6tQpxo8fzy+//EJOTg6mpqZ07dqV9PR0Tp06xdKlS8uM/z4XFxf9\nn+3s7Jg/fz5DhgzB2dm5YokTQgghhBCihpICqxrZ29uTkpLC5MmTqV+/Pjqdjv79+7Nhw4YHzo2J\nieH7778nMTGR3bt361/VMyQTExOsra2JjY3F3t4eOzs7UlJSyMrKomPHjmRlZZUZP0CDBg30fw4P\nD+fHH3/kq6++YuvWrcTHx1Ovnvw4CiGEEEKI2k3+RVuNRo0axalTp/Dz82Pz5s306tWLlStXcuXK\nFZ5//nny8vL4/fffadGiBfn5+QwaNIjevXtXeManffv2XLt2TT9uXFwcffv2LfN8BwcH/P39mTZt\nGjqdjmPHjrFu3Tr9scjISNasWYOtrS1r166lW7duKBSKMuO/P3N2n1ar5caNG/Tr148+ffqQkJBA\nXl4eTZo0KTMmrU7LF+4V//ZMPB3y1Q//plAIIYQQwlCkwKoi9+7dY+DAgfptX1/fUs/z9fVFpVKx\ncOFCgoODCQoK4t1339U3oXjnnXdo2LAhM2fOpKCgeJ0ff3//CsVSv359goKC8PPzQ6PR0L1794d2\nNOzWrRuenp6MHj0aKC4Eu3btChQXWFu3bqVXr16Ym5tTv359HBwcALC0tCw1/n8WWBqNhgULFpCT\nk4NOp2PSpEkPLa4A0Cm49YeqQs8tKq6uLPwnhBBCCFFdFDqdTlfdQQjxKFqdFiOFNL18muWrC1Hd\nqRuLCUshaxiSZ8OQPBuG5NkwJM+GUVfybGXVuNT9MoMlagUjhREuBwKrOwxRjb7wWIqKulFgCSGE\nEKLuqrMFlp2dHb6+vvrX6bZt20ZeXh6zZ8+usnv6+/tz8uRJGjcurma9vLz0a0WVxsnJiZiYGCwt\nLbG3tyctLa3c9woLCyMxMbHEvmHDhuk7EgohhBBCCCEMr84WWKamphw5coRp06ZhaWlpsPsuXLiQ\nYcOGVfl9ZsyY8VjFlEajwdjYuAoiEkIIIYQQQtTZAqtevXqMHTuWqKgo5s6dW+JYUlISYWFhqNVq\nLCwsCA4Opnnz5oSGhqJUKrl69So3btxg0aJF/Pzzz3z77be0aNGCrVu3YmJiQnp6OmvXriUvL49m\nzZoRFBREixYtyozl0KFDfPLJJ+h0OgYNGsSCBQvKPFen07Fu3Tq+/fZbFAoFM2bMwMXFhYCAAAYM\nGMCQIUN4++2P4KI5AAAgAElEQVS3adKkCUFBQcTExHD16lXmzp1LXFwcu3btQq1W88ILL7BixQqM\njY2xt7dn7NixnDhxguXLl/P111+TlJSEsbExAwYM4L333is1Fn9/f+rXr8+5c+e4ffs2a9as4eDB\ng/z888+88MILrF27FoDvvvuO0NBQCgsLadOmDUFBQTRs2JDNmzfz1VdfUVBQgL29PStXrkShUDBx\n4kR69uxJSkoKKpWK1atX6xtlCCGEEEIIUZvV6a4BEyZMID4+HpWqZPe5Pn36EB0dzcGDBxkxYgQR\nERH6Y1lZWURFRREWFsaCBQtwdHQkPj4eMzMzkpOTUavVBAYGEhISQmxsLF5eXmzcuFF//bp163Bz\nc8PNzY0LFy7w+++/ExwcTFRUFAcPHuTMmTMcO3aszJiPHDnC+fPniYuLY/v27axbt46bN2/i4ODA\nqVOnAPj999+5fPkyULwosIODA5cvX+bw4cPs3buXuLg4jIyMiI+PByAvL4+ePXvy+eef07FjR44e\nPUpCQgLx8fGPnAW7e/cu+/btY9GiRcyYMQMfHx8SEhK4ePEi586dIzs7m7CwMLZv386BAwfo3r07\n27dvB+DNN99k//79HDp0iPz8fL766iv9uBqNhpiYGBYvXszmzZvL89cphBBCCCFEjVdnZ7AAGjVq\nhJubGzt37sTMzEy//7fffmPu3LncunWLwsJCrK2t9ccGDhyIiYkJtra2aDQafat1W1tblEolmZmZ\nXLx4Ud92XavVYmVlpb/+n68IHjt2jBdffFH/mqKrqys//vhjmWtZpaamMmLECIyNjWnevDl9+/bl\nzJkzODg4EBUVRUZGBp06deKvv/7i5s2bpKWlsWTJEg4ePEh6ejqjRo0CID8/n2eeeQYAY2NjXnvt\nNQAaN25M/fr1Wbx4Ma+88gqDBw9+aA5feeUVFAoFdnZ2NG/eHDs7OwA6derEtWvX+O2338jIyNC3\nfVer1fTq1QuAlJQUIiIiyM/P586dO9jY2ODk5ATA0KFDgeKW8NeuXXtoDEIIIYQQQtQWdbrAAvD2\n9sbT0xNPT0/9vsDAQHx8fBgyZAgpKSklZlBMTU0BMDIywsTEBIVCod/WaDTodDpsbGzYt2+fQZ+j\nZcuW3L17l2+//RYHBwf++usvDh8+jLm5OY0aNUKn0+Hh4cG8efMeuLZ+/fr6767q1atHTEwM33//\nPYmJiezevZudO3eWed/7+VAoFPo/Q3E+ioqKMDIyon///mzYsKHEdQUFBQQEBLB//36ee+45QkND\n9et4/X3c+3kVQgghhBCiLqjzBZaFhQXDhg0jJiYGLy8vAFQqFS1btgTg4MGDFRqvffv2ZGdnk5aW\nhr29PWq1ml9//RUbG5tSz+/ZsyerV68mOzubpk2bkpCQwJtvvlnm+A4ODuzbtw8PDw/++usvTp06\nxcKFCwHo1asXUVFRREVFcefOHebMmaOfmXrppZeYOXMmPj4+PPPMM9y5c4fc3Fxat25dYvzc3Fzy\n8/MZNGgQvXv3LnMmrbx69erFypUruXLlCs8//zx5eXn8/vvv+tmzZs2akZuby5dffqmP9XFodVq+\n8Fj6RLGK2i1fXVjdIQghhBBCPFKdL7AAJk+ezJ49e/Tbs2bNws/Pj6ZNm+Lo6IhSqSz3WKampoSE\nhBAYGIhKpUKj0eDt7V1mgdWiRQvmzZuHt7e3vsnFw4qaoUOHkpaWhpubGwqFggULFuhfQezTpw/f\nffcdzz//PK1ateKvv/7SN4fo1KkT77zzDpMnT0ar1WJiYsLy5ctLLbBmzpypn02638b+cVlaWhIU\nFMS7775LYWHxP4Dfeecd2rdvz+jRoxk5ciTNmzenR48eT3QfdApu/aF69HniidSVhf+EEEIIIaqL\nQqfT6ao7CCEeRavVcft2TnWHUedJgWUYkmfDkDwbhuTZMCTPhiF5Noy6kmcrq8al7n8qZrBEHaAo\n+4dYVK6amOd8tRrVnfzqDkMIIYQQ4pGkwKpF/vjjD4KCgvj5559p2rQpJiYmTJ06Vd+R73GFhYWR\nmJhYYt+wYcPKbOEeGhqKubk5U6ZMKXPMY8eO0a5dOzp16gTARx99RN++ffnXv/71WDEaKRSMiP3w\nsa4VtV+C5zxUSIElhBBCiJpPCqxaQqfT8fbbb+Pu7s6HHxYXGteuXSMpKemJx542bdoj18OqqGPH\njjF48GB9geXn51ep4wshhBBCCFETSYFVS/zwww+YmJjo15sCaN26NRMnTkSj0RAcHMzJkycpLCxk\nwoQJjBs3Tt+CvlmzZly8eJFu3boRHByMQqHAycmJ4cOHc+LECaZOnUqPHj0ICAjgzz//xMzMjFWr\nVtGxY8dHxhUdHc2+fftQq9U8//zzrFu3jnPnzpGUlMTJkycJCwsjNDSULVu2MHjwYIYNG4aTkxPu\n7u589dVXFBUVsWnTpnLdSwghhBBCiJpOCqxa4tKlS3Tt2rXUYzExMTRu3Jj9+/dTWFjIuHHj6N+/\nPwBnz54lISGBFi1aMH78eFJTU/WdBy0sLDhw4ABQvF5YQEAA7dq14/Tp0wQEBDx0faz7hg4dypgx\nYwDYuHEjMTExTJw4EScnJ31BVZpmzZpx4MAB9uzZQ2RkJKtXr65wToQQQgghhKhppMCqpQICAkhN\nTcXExITWrVtz4cIFvvzyS6B4na8rV65gYmJCz549efbZZwHo3Lkz165d0xdYLi4uQHHr9rS0tBKv\n8d1vuf4oly5dYtOmTahUKnJzcxkwYEC5rnv11VcB6N69O0ePHi3fQwshhBBCCFHDSYFVS9jY2HDk\nyBH99ooVK8jOzmbUqFG0atWKpUuX8vLLL5e4JiUlBVNTU/22sbExGo1Gv92gQQOg+PuuJk2aEBcX\nV+G4/P392bJlC507dyY2NpaTJ0+W6zoTExMAjIyMSsQkhBBCCCFEbWZU3QGI8unXrx8FBQV8+umn\n+n35+cVd1QYMGMDevXtRq9UAZGZmkpdX/rUFGjVqhLW1NYcPHwaKC67z58+X69rc3FysrKxQq9XE\nx8fr9zds2JDc3NxyxyCEEEIIIURdIDNYtYRCoeDjjz8mKCiIiIgILC0tadCgAfPnz2fYsGFcu3YN\nT09PdDodzZo1Y8uWLRUaf/369bz//vuEhYVRVFSEi4sLnTt3fuR1fn5+jB49GktLS1544QV9UeXi\n4sKyZcvYtWsXISEhj/XMf6fV6UjwnPfE44jaKf+/vzwQQgghhKjpFDqdTlfdQQjxKFqtjtu3c6o7\njDqvrqysXtNJng1D8mwYkmfDkDwbhuTZMOpKnq2sGpe6X2awRO2gKPuHWFSumpjnfLUa1R1ZaFgI\nIYQQNZ8UWFWkS5cu2Nra6rc//vhjrK2tSz03JSWFyMhIPvnkE2JjY0lPT2f58uWGCrVMYWFhJCYm\nApCRkUG7du0YOXKkflHi48ePc/nyZaZNm4a/v/9D27I/KSOFghGxm6tkbFHzJXjOQoUUWEIIIYSo\n+aTAqiJmZmaP1ZXPUIqKiqhX7+F//TNmzNAXU05OTuzatQtLS0v98SFDhjBkyJAqjVMIIYQQQoja\nRLoIGlBBQQGLFi3C1dUVd3d3fvjhh4eer1QqmTRpEq6urnh7e3P9+nU0Gg1OTk7odDru3r1Lly5d\n+PHHHwGYMGECv/76K3l5eSxatIhRo0bh7u7OsWPHAIiNjeWtt95i0qRJ+Pj4cPPmTSZMmICbmxsj\nR47k1KlTFXqe2NhYVq5c+cD+TZs24e/vj0ajIT09nTfffBNPT0+mTJnCzZs3Adi5cycuLi64uroy\nd+7cCt1XCCGEEEKImkpmsKpIfn4+bm5uAFhbW/Pxxx+zZ88eAOLj47l8+TJTpkzRLw5cmsDAQDw8\nPPDw8CAmJobAwEC2bNlC+/btycjIQKlU0rVrV06dOsULL7zAjRs3aNeuHRs2bKBfv34EBQVx9+5d\nRo8ezb/+9S8Azp49y+eff46FhQWRkZEMGDCAGTNmoNFouHfv3hM/9wcffEBubi5BQUEUFRXpY7a0\ntOSLL75g48aNBAUFER4eTlJSEqampty9e/eJ7yuEEEIIIURNIAVWFSntFcHU1FTefPNNADp27Eir\nVq3IzMwsc4y0tDRCQ0MBcHNzY/369QA4ODjw448/olQqmT59OtHR0fTt25cePXoA8N1335GUlERk\nZCRQPHN248YNAPr374+FhQUAPXr0YPHixRQVFeHs7EyXLl2e6Jm3bNnCCy+8wKpVq4Di9bguXryI\nr68vAFqtFisrKwDs7OyYP38+Q4YMwdnZ+YnuK4QQQgghRE0hBVYt1LdvX/bu3cvNmzfx8/Nj27Zt\nnDx5EgcHB/05ISEhdOjQocR1p0+fpkGDBiXG2b17N8nJyfj7++Pr64u7u/tjx9WjRw9++eUX7ty5\ng4WFBTqdDhsbG/bt2/fAueHh4fz444989dVXbN26lfj4+Ed+EyaEEEIIIURNJ99gGZCDgwPx8fFA\n8ezOjRs3HiiC/s7e3p6EhASg+LXC+wVUz549SUtLQ6FQUL9+fTp37sy+ffvo27cvAAMGDGD37t3c\nX+Ls7NmzpY5/7do1mjdvzpgxYxg9ejS//PLLEz3fyy+/zP/8z/8wffp0cnJyaN++PdnZ2aSlpQGg\nVqu5dOkSWq2WGzdu0K9fP+bPn49KpSIvr/avhSCEEEIIIYRMGRjQG2+8wfvvv4+rqyvGxsYEBQVh\nampa5vnLli1j0aJFbNu2DUtLS4KCggAwNTXl2WefpVevXkBx4ZaQkKBvCz9z5kzWrFnD66+/jlar\nxdramk8++eSB8U+ePMm2bduoV68e5ubmfPDBBw+N//XXX8fIqLgmHz58OHZ2dg+cM3z4cHJzc5kx\nYwb//ve/CQkJITAwEJVKhUajwdvbm3bt2rFgwQJycnLQ6XRMmjSJJk2aPPTeWp2OBM9ZDz1H1F35\nanV1hyCEEEIIUS4K3f1pDiFqMK1Wx+3bOdUdRp1XV1ZWr+kkz4YheTYMybNhSJ4NQ/JsGHUlz1ZW\njUvdLzNYonZQlP1DLCpXTcxzvlqN6o4sNCyEEEKImk8KLFHC6NGjKSwsLLFv3bp1pb4OaEhGCgUj\n9odXawyi+iR4TUOFFFhCCCGEqPmkwKpEdnZ2+Pr64u/vD8C2bdvIy8tj9uzZVXZPf39/Bg8ezLBh\nw/T7fv/9d1avXk1ISAixsbGkp6ezfPnyco332WefVVWoQgghhBBC1Hnl6iKYmZmJt7c3I0eOBOD8\n+fNs2bKlSgOrjUxNTTly5AjZ2dnVGkfLli0JCQmp1hiEEEIIIYR4GpWrwFq2bBnz5s3Tr1PUuXNn\nvvjiiyoNrDaqV68eY8eOJSoq6oFjSUlJjB49Gnd3d3x8fPjjjz8ACA0N5b333uONN97glVde4ciR\nI6xbtw5XV1emTJmC+r/d09LT03nzzTfx9PRkypQp3Lx5s8w4lEqlvhj+u6+//pqxY8eSnZ1NdnY2\ns2fPxsvLCy8vL1JTU4HizoJubm64ubnh7u5OTk7pjSVu3rzJhAkTcHNzY+TIkZw6dQoobi1/X2Ji\non42z9/fnxUrVjBmzBiGDBlCSkoKixYtYvjw4fpzhBBCCCGEqO3KVWDdu3ePnj17lthnbGxcJQHV\ndhMmTCA+Ph6VSlVif58+fYiOjubgwYOMGDGCiIgI/bGsrCyioqIICwtjwYIFODo6Eh8fj5mZGcnJ\nyajVagIDA/Wv/Hl5ebFx48YKxXX06FHCw8MJDw/H0tKS1atX4+3tzf79+wkNDWXp0qUAREZGsnz5\ncuLi4tizZw9mZmaljnfo0CEGDBhAXFwccXFxdO7c+ZEx3L17l3379rFo0SJmzJiBj48PCQkJXLx4\nkXPnzlXoeYQQQgghhKiJyvUNVrNmzcjKykKhUADFMxNWVlZVGlht1ahRI9zc3Ni5c2eJ4uS3335j\n7ty53Lp1i8LCQqytrfXHBg4ciImJCba2tmg0GgYOHAiAra0tSqWSzMxMLl68iK+vLwBarbZC+f/h\nhx9IT08nMjKSRo0aAXDixAkyMjL05+Tk5JCbm0vv3r1Zu3Ytrq6uvPrqqzRs2LDUMXv06MHixYsp\nKirC2dmZLl26PDKOV155BYVCgZ2dHc2bN9c3zujUqRPXrl0r1xhCCCGEEELUZOUqsFasWMGyZcv4\nf//v//Hyyy9jbW1NcHBwVcdWa3l7e+Pp6Ymnp6d+X2BgID4+PvrX4zZv3qw/dn+xYSMjI0xMTPSF\nrJGRERqNBp1Oh42NDfv27XuseNq2bcvVq1fJzMykR48eQHGRFh0dTf369UucO23aNAYNGkRycjLj\nx48nIiKCjh07PjBm37592b17N8nJyfj7++Pr64u7u3uJcwoKCkps339OhUJRYoFlIyMjioqKHuvZ\nhBBCCCGEqEkeWWBptVrOnDnDjh07yMvLQ6vV6mdBROksLCwYNmwYMTExeHl5AaBSqWjZsiUABw8e\nrNB47du3Jzs7m7S0NOzt7VGr1fz666/Y2NiU6/pWrVqxYMECZs+ezUcffYSNjQ0DBgxg165dTJ06\nFYBz587RpUsXsrKysLOzw87OjvT0dDIzM0stsK5du8azzz7LmDFjKCws5JdffsHd3Z3mzZtz+fJl\n2rdvz7Fjx8qcAasorU5Hgte0ShlL1D75//0WUQghhBCipntkgWVkZERERAQuLi6Ym5sbIqY6YfLk\nyezZs0e/PWvWLPz8/GjatCmOjo4olcpyj2VqakpISAiBgYGoVCo0Gg3e3t76AmvFihWsWbMGgOee\ne44PP/zwgTE6duxIcHAwfn5+bN26lSVLlrBy5UpcXV3RaDQ4ODiwcuVKoqKiSElJQaFQYGNjo39d\n8Z9OnjzJtm3bqFevHubm5nzwwQcAzJs3j+nTp2NpaUn37t3Jy6ukVbp1cOsP1aPPE0+krqysLoQQ\nQghRXRQ6nU73qJOCg4Np1qwZLi4uNGjQQL/fwsKiSoMT4j6tTofRf1+dFE+HfLUa1Z26ubiwFLKG\nIXk2DMmzYUieDUPybBh1Jc9WVo1L3V+ub7Dut2T/+4yMQqHg+PHjlRCaEI9mpFAwcv+O6g5DGNAh\nLx9U1M0CSwghhBB1V7kKrKSkpKqOo07o0qWLvhNghw4d+OCDD0rM+D2p2NhY0tPTWb58ebmvOXPm\nDHFxcSxdupSUlBRMTEzo3bt3ua+/cOECCxcu5Pr16zRq1IgmTZpgamrKZ599xu+//87q1av17eMr\nGpsQQgghhBB1TbkKrLKaMvyza9zTzszMjLi4OKD4W6T//Oc/+tbq1aGoqIgePXroOweePHkSc3Pz\nChVYdnZ2xMXF4e/vz+DBgxk2bJj+WMuWLQkJCan0uIUQQgghhKityrXQ8JkzZ/T/nTp1itDQUJnV\negQHBweuXLkCwPbt2xk5ciQjR45kx44dACiVSoYNG8a8efMYPnw4c+bM4d69ewA4OTmRnZ0NFOd+\n4sSJD4yflJTE6NGjcXd3x8fHhz/++AOA0NBQFixYwLhx41i4cCEpKSlMnz4dpVLJf/7zH3bs2IGb\nmxunTp3CyckJ9X+7s+Xk5JTYLg+lUsnIkSMf2P/1118zduxYsrOzyc7OZvbs2Xh5eeHl5UVqaipQ\nXOy5ubnh5uaGu7s7OTk55b6vEEIIIYQQNVW5ZrCWLVtWYvvu3bvMnTu3SgKqC4qKivjmm294+eWX\nSU9PJzY2lujoaHQ6HWPGjOHFF1+kSZMmZGZmsnr1avr06cOiRYv49NNPmTJlSrnu0adPH6Kjo1Eo\nFHz22WdERETg7+8PwOXLl/n0008xMzMjJSUFAGtra8aNG4e5ubn+Ho6OjiQnJ+Ps7ExCQgKvvvoq\nJiYmT/TsR48eZfv27YSHh9O0aVPmzZuHt7c3Dg4OXL9+nSlTpnD48GEiIyNZvnw5ffr0ITc394H1\nuIQQQgghhKiNylVg/VODBg0q1Gb8aZGfn4+bmxtQPIM1atQo9u7di7Ozs77F/dChQ/WzR8899xx9\n+vQB4PXXX2fXrl3lLrB+++035s6dy61btygsLMTa2lp/zMnJCTMzs0eOMWrUKCIiInB2diY2NpZV\nq1ZV9JFL+OGHH0hPTycyMlK/VtqJEyfIyMjQn5OTk0Nubi69e/dm7dq1uLq68uqrr1baellCCCGE\nEEJUp3IVWG+99Zb+zzqdjoyMjBLf4ohif/8GqzwU/2g7fn/b2NiY+93zCwoKSr02MDAQHx8fhgwZ\nQkpKCps3b9YfK29jjT59+hAQEEBKSgoajQZbW9tyx16atm3bcvXqVTIzM/XffWm1WqKjox+YoZo2\nbRqDBg0iOTmZ8ePHExERUeqCxkIIIYQQQtQm5SqwJk+erP+zsbExrVu35tlnn62yoOoSBwcH/P39\nmTZtGjqdjmPHjrFu3ToArl+/TlpaGvb29hw6dEg/m9W6dWvS09MZNGgQR44cKXVclUpFy5YtgbKb\nkPxTw4YNH/jWyd3dnXnz5jFz5szHfUS9Vq1asWDBAmbPns1HH32EjY0NAwYMYNeuXUydOhWAc+fO\n0aVLF7KysrCzs8POzo709HQyMzMfWmBpdToOefk8cYyi9sivwPeAQgghhBA1RbkKrOTkZBYsWFBi\n3/r16x/YJx7UrVs3PD09GT16NFD8Wl7Xrl1RKpW0b9+ePXv2sHjxYjp16sT48eMBmDVrFkuWLOGj\njz7C0dGx1HFnzZqFn58fTZs2xdHRsVyvbL7yyivMmTOH48ePs2zZMhwcHHB1dWXTpk2lNqv4pxUr\nVrBmzRoAnnvuOT788MMHzunYsSPBwcH4+fmxdetWlixZwsqVK3F1dUWj0eDg4MDKlSuJiooiJSUF\nhUKBjY0NAwcOfPjNdXDrD9UjYxRPpq4s/CeEEEIIUV0Uuvvvoj2Eh4cHBw4cKLHP1dWV+Pj4Kgus\nrlMqlbz11lscOnSoWuNITEzk+PHjrF+/vlrjeBStTofRP16pFHVbvlqN6k7dXGhYClnDkDwbhuTZ\nMCTPhiF5Noy6kmcrq8al7n/oDNann37K3r17uXr1Kq6urvr995sUiNpt1apVfPPNN4SHh1d3KI9k\npFAwMmZPdYchDOjQqAmoqJsFlhBCCCHqrocWWK6urgwcOJANGzYwb948/f6GDRtiYWFR5cFVFjs7\nO3x9ffVtzLdt20ZeXh6zZ8+u0vtu27aNzz77jPr161OvXj0mTpyoX5zZ2tq62mev/t5+/+7du8TH\nx5ORkcFPP/1U4rxJkybh5eX1RPc6c+YMcXFxLF269InGEUIIIYQQoiZ7aIHVuHFjGjduzIYNGwC4\nffs2BQUF5OXlkZeXR6tWrQwS5JMyNTXlyJEjTJs2DUtLS4Pcc+/evZw4cYKYmBgaNWpETk4OR48e\nNci9H8fdu3fZu3dvlRV9PXr00HcWFEIIIYQQoq4qV5OLpKQk1q5dy82bN7G0tOT69et07NiRhISE\nqo6vUtSrV4+xY8cSFRX1wALJSUlJhIWFoVarsbCwIDg4mObNmxMaGopSqeTq1avcuHGDRYsW8fPP\nP/Ptt9/SokULtm7diomJCenp6axdu5a8vDyaNWtGUFAQLVq04JNPPmHXrl369aAaNWqEh4cHAN9/\n/z0ffPABGo2G7t27ExAQgKmpKU5OTowYMYJvvvkGY2NjVq1axYYNG7hy5QpTpkxh/PjxpKSkEBoa\nSuPGjbl48SLDhw/H1taWnTt3UlBQwMcff0zbtm3Jzs5mxYoVXL9+HYDFixfTp08fQkNDuX79Okql\nkuvXr+Pt7c2kSZP48MMPycrKws3NjX/961/4+voyd+5ccnJy0Gg0vP/++zg4OJSaX3t7e8aNG8c3\n33yDlZUV7777LuvXr+f69essXrxY30o+MjKSTz75pMwYhBBCCCGEqO2MynPSpk2b2LdvH+3atSMp\nKYkdO3bwwgsvVHVslWrChAnEx8ejUpXsRNenTx+io6M5ePAgI0aMICIiQn8sKyuLqKgowsLCWLBg\nAY6OjsTHx2NmZkZycjJqtZrAwEBCQkKIjY3Fy8uLjRs36hfTbdOmzQNxFBQU4O/vz8aNG4mPj0ej\n0fDpp5/qjz/33HPExcXp27t/9NFHREdHExoaqj/n/PnzBAQEcPjwYeLi4vj/7N15VJTl+/jx9wzM\niIALmEtK5Aqa4W5qIiRaqTiCgGspouUWbmGuuW9ZuCKS6yc1c2EdkVxbXMvULNc0DVHc0LAcQIEZ\n5vcHP5+vJCiYgtD1Oudzzmee5b6v59LT6e5+nuu6ePEiERER+Pn5sW7dOgBmzZqFv78/kZGRhISE\n5Hg1Lz4+Xnl9MTQ0lMzMTIKCgnB0dESv1zN27Fi2bt2Kq6srer0evV5P3bp188xtWloaLVu2JC4u\nDhsbGxYuXMjq1asJDQ1l8eLFud6TWwxCCCGEEEIUd/nawbK0tMTOzo6srCyysrJo2bKlUq67uLC1\ntcXLy4u1a9diZWWlHL9+/TqjRo3i5s2bZGRk4ODgoJxzc3NDo9Hg5OSEyWRSSok7OTmRmJhIfHw8\n586dIyAgAMhuqluxYsVHxhEfH4+DgwM1atQAsis0rl+/nn79+gHQrl07ZY60tDRlB0yr1XLnzh0g\n+3W7SpUqAdnNfVu3bq3cc+jQIQAOHjzI+fPnlXnvL/oA3N3d0Wq12NvbY29vz59//vlQnC4uLkyY\nMAGj0Uj79u2pV69ens+k0Why5Ear1Sp5u3LlSq735BaD9FYTQgghhBDFXb4WWGXLliU1NZVmzZox\nevRo7O3tsba2ftaxPXX+/v74+Pjg4+OjHJs5cyb9+vVTXmNbsmSJck6r1QKgVqvRaDSo/n+ZcLVa\njclkwmw2U6dOHTZt2vTQXNbW1ly+fDnXXaxH0Wg0yhz357//22g05ojrn9fdjwuyF3ubN2+mVKlS\nD83x4P0WFhbKuA9q3rw5X375JXv27GHcuHEEBAQoBTpyi/nB3OQWz5PEIIQQQgghRHGTrwXW0qVL\nsbKyYuH2l+0AACAASURBVMKECcprdh988MGzju2pK1++PB06dCAiIkKpimcwGKhcuTIAMTExBRqv\nRo0aJCcnc+zYMRo3bkxmZiYXL16kTp06DBw4kGnTprFw4UJsbW1JTU1l165ddOzYkStXrpCQkMDL\nL7+MXq+nefPmT/1ZXV1dWbduHe+99x4AZ86ceeQulI2NjbLDBXDlyhWqVKlC9+7dycjI4NSpU3ku\nsApDltnMVr93imx+UfjuyWujQgghhCiG8rXAsra2VhYFXbt25e7du3nuTDzv+vfvz/r1/9dPKTAw\nkBEjRlCuXDlatGhBYmJivsfSarUsXryYmTNnYjAYMJlM+Pv7U6dOHXr37k1aWhq+vr5oNBosLS0J\nCAigVKlSzJkzhxEjRihFLnr16vXUn3PixIlMnz4dnU6HyWSiWbNmTJ8+Pc/r7ezsaNKkCZ07d6ZN\nmzY4OTmxatUqLC0tsba2Zu7cuU89xgIxw81bhsdfJ/6VktL4TwghhBCiqKjMZrP5cRdt3ryZTZs2\n8ffff7N7924uXrzIlClTWLNmTWHEKARZZjPq//8aoij57mUaMfx1t6jDeGZkIVs4JM+FQ/JcOCTP\nhUPyXDhKSp4rViyT6/F87WCtX7+e8PBwunfvDkD16tVJTk5+etEJ8RhqlYrOEZuLOgxRSLb6dUf2\nK4UQQghRHOVrgaXVanMUJSjuBQnq1auHk5OT8js0NDRH9cAHPdi/KSoqipMnTzJ58uTCCjVfPDw8\nsLGxAaBixYrMnTv3sdUMC+J+DpKTk8nIyMhx7tNPP8XZ2fmhe27cuMGsWbNYvHgxZ86cISkpCXd3\n96cWkxBCCCGEEM+jfC2wmjdvzueff869e/c4cOAAX331FR4eHs86tmfGysoKvV5f1GHkyWg0YmmZ\nrz8axZo1a7C3t2f+/PksW7YsR9+rpyU8PDxf1xmNRipXrqz0wDpz5gwnT56UBZYQQgghhCjx8tVo\n+H5pdicnJzZt2oS7uzsjR4581rEVqvT0dMaPH49Op8Pb25sff/zxkdcnJibSt29fdDod/v7+XL16\nFZPJhIeHB2azmTt37lCvXj0OHz4MZDc6vnjxImlpaYwfPx4/Pz+8vb3ZvXs3AFFRUQwePJi+ffvS\nr18/kpKSeOedd/Dy8qJz584cOXIkX8/RrFkzEhISANi6dSs6nY7OnTvz2WefKdc0btyY2bNn4+np\nib+/v/K6Z58+fThx4gQAycnJuS6ijx8/To8ePfD29qZnz5788ccfucafmJhI586dycjIYPHixXz9\n9dd4eXnx9ddf89ZbbylzZmVl8eabb8orp0IIIYQQokR45DbJ1atXqVq1Kmq1mu7duyvfYBV39+7d\nw8vLCwAHBwdCQ0OVyoKxsbFcuHCBAQMGsGPHjjzHmDlzJl27dqVr165EREQwc+ZMli5dSo0aNTh/\n/jyJiYm88sorHDlyhIYNG3Lt2jWqV6/O/PnzadmyJXPmzOHOnTt069aN119/HYDTp0+zZcsWypcv\nz+rVq3F1dWXIkCGYTCbu3s3fB//ff/89Tk5O3Lhxg+DgYKKioihbtiz9+/dn9+7dtG/fnrS0NF59\n9VUmTJjAkiVLWLJkSb5fe6xZsybr16/H0tKSgwcPsmDBAkJCQh6K/341Rq1Wy/Dhw3O8WvnHH3+w\nZcsW+vXrx8GDB6lbty729vb5ml8IIYQQQojn2SN3sB7sdTVs2LBnHkxhuf+KoF6vJzQ0FICjR4/S\npUsXAGrVqkXVqlWJj4/Pc4xjx47RuXNnALy8vDh69CiQvYN0+PBhDh8+zKBBgzh69CjHjx/HxcUF\ngP3797NixQq8vLzo06cP6enpXLt2DYDWrVtTvnx5AFxcXIiKiiIkJIRz585ha2v7yGfy9/fHy8uL\nlJQUBg0axIkTJ3jttdewt7fH0tISnU6n7Kap1Wo6der0UOz5YTAYGDFiBJ07d2bOnDn8/vvvyrkH\n438UX19f5RXNyMjIHI2fhRBCCCGEKM4eucB6sIL75cuXn3kwJUHz5s05evQoJ06cwN3dHYPBwE8/\n/USzZs2UaxYvXqws8L7//ntq1aoFQOnSpXOM8+WXX1K5cmXGjRv32CbIa9asQa/X8+mnn1K2bNkC\nxaz6/+XPLSwslD/zfxazuG/RokW0aNGCrVu3EhYWluO6B+N/lBdffJEKFSrwww8/cPz4cdzc3AoU\nrxBCCCGEEM+rR74iqHqg75CqhPcgatasGbGxsbRq1Yr4+HiuXbtGzZo1OXbsWK7XN27cmLi4OLy9\nvYmNjVUWUA0aNGDMmDE4ODhQqlQp6taty6ZNm1i2bBkArq6ufPnll0yaNAmVSsXp06d55ZVXHhr/\nypUrVKlShe7du5ORkcGpU6fw9vbO9/M0aNCAWbNmkZycTLly5YiLi+Pdd98Fsr972rFjB56ensTG\nxtK0aVMAqlWrxsmTJ2nQoAHbt2/PdVyDwUDlypUBiI6OzlcsNjY2pKam5jjWrVs3PvroI7y8vLCw\nsHjsGFlmM1v9SsYrquLx7mUW70qlQgghhPjveuQC67fffqNJkyaYzWbS09Np0qQJkL2zpVKp+Pnn\nnwslyMLQu3dvpk6dik6nw8LCgjlz5uQoTf9PkyZNYvz48axatQp7e3vmzJkDZH9zVKVKFRo1agRk\nL9zi4uKUsvBDhw5l9uzZdOnShaysLBwcHJTF14N++uknVq1ahaWlJdbW1sydO7dAz1OpUiWCgoLw\n9/fHbDbj7u5O+/btAbC2tub48eOEhYVhb2/PwoULAejfvz8jR45k8+bNeVb8e++99xg3bhxhYWH5\nrgrYokULli9fjpeXF4MGDaJTp054eHgwfvz4/L8eaIabt6Qz0rNWUhr/CSGEEEIUFZX5wfcAxX9C\n48aN89yZKywnTpxgzpw5fPXVV/m6PstsRl3Cd1FFtnuZRgx/5a+oS3ElC9nCIXkuHJLnwiF5LhyS\n58JRUvJcsWKZXI8XrNmSeG7db55sMpmoWbMmc+fOzfc3UfnxJE2WT5w4gV6v5+OPP+bQoUNoNBqa\nNGnC8uXL2bBhQ47S8Y+jVqnQRTz6OzRRMsT6eSN7lUIIIYQormSBVYx069btoeITn376Kc7Ozjma\nJwcFBbFx40YCAgJyHacwdq+MRiMuLi5K9cSffvoJa2trmjRpwsCBAxk4cOAzj0EIIYQQQojCJgus\nYiQ8PDxf1zVr1oyzZ88C8L///Y/IyEgA/Pz8lCbA7733HvXr1+f06dPUqVNH2fHy8PAgIiICe3t7\nTpw4waeffsq6detyjP/tt98SFhZGZmYm5cuXJzg4mBdeeIGQkBAuXbrE5cuXqVq1Kj169GD16tVM\nmjSJjRs3olar2bJlC5MmTWLMmDHs2LEDjUZDSkoKXbp0UX4LIYQQQghRXD2yTLsofoxGI3v37sXJ\nyYmTJ08SFRXF5s2b2bRpE+Hh4Zw+fRqA+Ph4evfuzbZt27Cxscn3t1AATZs2ZfPmzcTExODp6cnK\nlSuVcxcuXOCLL75g/vz5yjEHBwd69uxJv3790Ov1NGvWjBYtWrBnzx4A4uLieOutt2RxJYQQQggh\nij1ZYJUQ9+7dw8vLC19fX6pWrYqfnx9Hjx6lffv2WFtbY2Njw5tvvsmRI0eA7F5U98uzd+nSpUDN\nhq9fv86AAQPQ6XSsXLkyR7NhDw8PrKysHjuGn5+fsrMWFRUlzYaFEEIIIUSJIK8IlhAPfoOVH//s\na5Zbs+H09PRc7505cyb9+vWjXbt2HDp0iCVLlijn8ltYo2nTpkybNo1Dhw5hMpmUMvZCCCGEEEIU\nZ7KDVYI1a9aM3bt3c/fuXdLS0ti9e7fSEPnq1atKsYutW7c+1GwYYOfOnbmO+2Cz4ZiY/FX2y63Z\nsLe3N0FBQbJ7JYQQQgghSgxZYJVg9evXx8fHh27dutG9e3f8/Px45ZVXAKhRowbr16+nY8eO3Llz\nh169egEQGBjI7Nmz8fHxwcLCItdxAwMDGTFiBD4+PpQvXz5fsbRt25Zdu3bh5eWlvKao0+m4c+cO\nnTt3fgpPK4QQQgghRNGTRsP/QYmJiQwePJitW7cWaRzbt2/nm2++yVc/LGk0/N8hjYbF0yJ5LhyS\n58IheS4ckufCUVLyLI2GxXNlxowZ7N27l+XLl+fvBjPcvCXtZ5+1kvIPPCGEEEKIoiI7WKJYkB2s\n/4b/wu4VyEK2sEieC4fkuXBInguH5LlwlJQ8F+oOltlspnfv3gwePBh3d3cAtm3bRkREBKtWrXrq\n840ePZrDhw+za9cutFotN2/epHfv3uzateupz/U4v/zyC3PnzuX27dtYWVnh4uLCxIkT81W6vChE\nRETg7u5OxYoVizqUR1KrVHhFfF3UYYhnTO/XCdmnFEIIIURx9kyKXKhUKqZNm8Ynn3xCeno6qamp\nLFiwgClTpvyrcY1G4yPnzG9Fu2clKSmJUaNGMX78eLZv3050dDStWrUiLe35XaFHRkZy69atog5D\nCCGEEEKIEuGZfYPl5ORE27ZtWbFiBWlpaXh5eeHo6Eh0dDTr168nMzOTxo0bM3nyZNRqNZMmTeLU\nqVOkp6fTsWNHAgMDAXBzc6NLly7s37+fQYMG0bFjx1zn69evH6tWrcLX1zfH8ZSUFIYOHYrBYMBo\nNPLhhx/Stm1bEhIS+OCDD6hXrx7Hjx+nYcOG6HQ6QkNDSU5OZt68ebi4uJCamsqMGTM4f/48RqOR\n4cOH4+HhkWsMX375Jb6+vjRo0ADIXvR16tQJgOTkZCZMmMCVK1ewsbFh+vTpODk5sWDBAm7cuEFC\nQgLXr19n4sSJHDlyhP3791O1alWWLl2KpaUlbm5ueHt78/3336PRaJg+fTrz5s3j0qVLDBw4kO7d\nuwOwfPlydu7cSXp6Om+//TaBgYHKszZo0IBff/2VF198kdDQUL755ht+++03Ro4ciZWVFeHh4SxY\nsIA9e/ZgYWGBm5sbH330Ua7PGhcXR1hYGGq1mnLlyrFu3TrCw8M5d+4cEydOBGDAgAEMGTKERo0a\n0bJlS3x9fdm/fz9VqlRh+PDhfPbZZ1y7do3JkycrO51CCCGEEEIUZ8+0THtgYCCxsbHs27eP999/\nn3PnzrFr1y42btyIXq/HZDIRFxcHQFBQEFFRUej1eg4ePMj58+eVcSpUqEBMTEyeiysABwcHGjZs\nSGxsbI7jpUqVYunSpURHR/PFF18wZ84c5Vx8fDyDBg1i27ZtnD17lp07d7Jx40aCgoJYsWIFAKGh\nobRp04aIiAjWrFnD3Llz82zAe+7cOV599dVczy1atEiJLzAwkHHjxinnEhMTWbduHSEhIQQFBdGm\nTRu2bt2KWq1m3759OZ5xy5YtNGrUiIkTJ7JkyRI2btzIokWLANizZw9Xr14lPDwcvV7PsWPH+Pnn\nn5Vn9ff3Jy4uDisrK3bv3k2nTp2oW7cuCxcuRK/Xc+fOHfbu3UtcXByxsbEMGjQoz3wvWbKEL774\ngi1bthAaGprndfcZDAbc3NyIi4tDo9EQEhLCF198waJFi5T4hRBCCCGEKO6eaRVBa2trOnXqhLW1\nNVqtloMHD3LixAlll+nevXtUqVIFyN4RiYiIwGg0kpSUxPnz56lduzaAsgv0OIMGDWLEiBG0atVK\nOWY2mwkODubo0aOo1WquXbtGcnIyAI6OjsoctWvXVu5zcnJi2bJlABw4cIB9+/Yp1e7S09O5evUq\nNWrUKFAufv75Z2VMV1dXxo0bp7w66ObmhqWlJU5OTgC0bt1aiePKlSvKGPd3zpycnDAajVhbW2Nt\nbY1KpSI1NZX9+/ezd+9evL29AUhLS+PixYtUqFABR0dHnJ2dgez+WA+Oe1+5cuVQq9V8/PHHvPHG\nG7zxxht5Pk+TJk0YO3YsHTp04M0333zs81tZWeV4LltbW+WZc4tFCCGEEEKI4uiZl2lXq9Wo1f+3\nUebr68vIkSNzXHPx4kXWrl1LeHg4ZcuWZfTo0Tl2iUqXLp2vuWrVqkWtWrVyFLfQ6/UYDAaio6OV\nV+0yMjIA0Gq1ynUqlUr5rVarMZlMQPYCLTQ0FEdHx8fOX6dOHU6ePPnIhUluHpxXo9HkiOnB784e\nvO7B2O/HazabGTJkCN26dcsxfkJCQo7rLSwscv2eTaPREBkZyYEDB9i+fTsbNmxg9erVucY8c+ZM\nfv31V7777jt8fHyIjo7GwsKCB4tS3s/z/bEffK7cci2EEEIIIURx90xfEfynVq1asW3bNmUH6fbt\n21y9epWUlBRsbGywtbUlKSmJ/fv3P/EcQ4YMyVGp0GAwUKFCBSwtLTlw4AA3btwo0Hiurq6sW7dO\n+X369Ok8r3333XeJjIzkxIkTQPbi7P7zNm3aVHl98eDBg1SuXBlra+sCxfI4bdq0ITIyUtkZu379\nupLrvNjY2JCamgpkf6+WkpJC27ZtGT9+/COf9fLlyzRq1IiRI0dStmxZbty4QbVq1Th9+jRms5nE\nxEROnjz59B5OCCGEEEKIYqBQGw07OzsTGBhIQEAAWVlZaDQapk6diouLC7Vq1aJjx45UrVqVJk2a\nPPEcdevWxdnZmQsXLgDg5eXF4MGD0el0uLi4UL169QKNFxgYyOzZs9HpdGRlZeHo6EhYWFiu11au\nXJng4GBmzZrFX3/9hUql4rXXXqNt27YMHz6cCRMmoNPpsLGxyfEt2NPi7u7OH3/8QY8ePYDsxVNw\ncPAj7/Hx8VHKyIeFhTF8+HAyMjIwm805vhP7p9mzZ3PlyhXMZjOtW7fGyckJs9lM5cqV6dixI3Xq\n1KFevXpP7dmyzGb0fvl7VVQUX/cy864UKoQQQghRHEijYVEsZGWZ+fPPlKIOo8QrKY3/nneS58Ih\neS4ckufCIXkuHJLnwlFS8lyojYaFeOpUef8lFk9XUeT5XqYRw193C31eIYQQQoinrVgtsCZPnsyv\nv/6a41hAQIBSNS8/nJ2dCQgIUF5/W7VqFWlpaQwbNizfY+zZs4f58+fnOPbyyy+zePHiXK8fN24c\nP/30E2XKlEGtVjN58mQaN26c7/nyo3Hjxhw7dqxA97z//vvMmzcPgNjYWN55552HrlmyZEmOoiEA\nnp6eDBw4sEBzLVq0iObNm/P6668X6L771CoV3hG7n+he8fyL8WuPoaiDEEIIIYR4CorVAmv69On/\negytVsvOnTsZOHAg9vb2TzSGu7t7gRvjjhkzhg4dOrB//34mT578UL+uwmQ2mzGbzUqvr8TERDZs\n2JDrAiswMFBp+vxvjBgx4l+PIYQQQgghxPOuWC2wngZLS0t69OjBmjVrGDVqVI5z3377LWFhYWRm\nZlK+fHmCg4N54YUXCAkJITExkcuXL3Pt2jXGjx/PL7/8wr59+6hUqRKff/45Go2GkydP8sknn5CW\nloadnR1z5syhUqVKOeZo3rw5ly5dAuDMmTNMmTKFu3fv4ujoyOzZsylXrhx9+vTB2dmZw4cPYzKZ\nmD17Ng0aNCAkJARra2sGDBgAQOfOnfn8889xcHBQxk9NTWXo0KHcuXMHo9HIiBEjaN++PYmJiQwY\nMICGDRty6tQpli9fTp8+fYiIiGDevHlcunQJLy8vXn/9df7880/eeust2rdvD2Q3ge7YsaPy+0FR\nUVHs3r2bu3fvkpCQQP/+/cnMzESv16PValm+fDnly5dn3LhxvPHGG3To0AEPDw+8vb357rvvMBqN\nLFy4kFq1aj3VP2chhBBCCCGKQqGWaX9evPPOO8TGxmIw5HwpqWnTpmzevJmYmBg8PT1ZuXKlcu7S\npUusWbOGsLAwPvroI1q0aEFsbCxWVlbs2bOHzMxMZs6cyeLFi4mKisLX15cFCxY8NPe3336rNBQe\nM2YMo0ePJjY2FicnJ5YsWaJcd+/ePfR6PVOmTGHChAn5frZSpUoRGhpKdHQ0a9asYe7cuUpvqoSE\nBHr37k1cXBzVqlVT7gkKCsLR0RG9Xs/YsWPx8/MjKioKyC5zf+zYsUf29vr9998JCQkhIiKCBQsW\nYGVlRUxMDI0aNSImJibXe+zs7IiOjqZnz5559toSQgghhBCiuPnP7WAB2Nra4uXlxdq1a7GyslKO\nX79+nVGjRnHz5k0yMjJy7Ay5ubmh0WhwcnLCZDLh5uYGgJOTE4mJicTHx3Pu3DkCAgIAyMrKomLF\nisr9n376KWFhYdjb2zNr1iwMBgMGg4HXXnsNgK5du+Z4jc7T0xPI3vFKSUnhzp07+Xo2s9nM/Pnz\nOXz4MGq1mhs3bnDr1i0AqlatSqNGjR47xmuvvca0adNITk5mx44dvP3221ha5v1XpUWLFtja2gJQ\npkwZPDw8lNycPXs213veeustAF599dWHvvESQgghhBCiuPpPLrAA/P398fHxwcfHRzk2c+ZM+vXr\nR7t27Th06FCOHSWtVguAWq1Go9GgUqmU3yaTCbPZTJ06ddi0aVOu893/Buu+f+6e/dP98R/8bWFh\nQVZWlnIsPT39oftiY2NJTk4mKioKjUaDh4eHcl1BGht7eXmxZcsW4uLiHtuz635u4P/yc///m0ym\nXO/JzzVCCCGEEEIUN//JVwQBypcvT4cOHYiIiFCOGQwGKleuDJDnq215qVGjBsnJyUolv8zMTH7/\n/fc8ry9Tpgxly5blyJEjAOj1epo3b66c//rrrwE4cuQIZcqUoUyZMlSrVo3Tp08DcOrUKRITEx8a\n12AwUKFCBTQaDT/++CNXrlx5bOw2NjakpqbmOObj48OaNWsAqF279mPHEEIIIYQQQvyHd7AA+vfv\nz/r165XfgYGBjBgxgnLlytGiRYtcFzB50Wq1LF68mJkzZ2IwGDCZTPj7+1OnTp0875k7d65S5OKl\nl17KsVNUqlQpvL29MRqNzJ49G4C3334bvV6Pp6cnDRo0oHr16g+NqdPpGDJkCDqdjldffZWaNWs+\nNnY7OzuaNGlC586dadOmDWPHjuWFF16gZs2auRa2KApZZjMxfs9HLOLpu5dpLOoQhBBCCCGeCpX5\nfgUE8dzo06cPY8aMwcXFpchiuHv3LjqdjujoaMqUKfoGv1lZZv78M6WowyjxSkpn9eed5LlwSJ4L\nh+S5cEieC4fkuXCUlDxXrJj7vyP/p3ewRO4OHjzIxIkT8ff3fy4WVwCo8v5LLJ6uwsjzvUwjhr/u\nPvN5hBBCCCEKm+xgCQCcnZ3R6XQEBwcDYDQacXV1pWHDhixbtoyQkBA2bdpEhQoVlHscHBwIDQ3N\n1/hffPEFPXr0oHTp0k8cY9fI/U98r3i+RPu6cvPmowu9lGQl5b/cPe8kz4VD8lw4JM+FQ/JcOEpK\nnmUHSzyStbU1v//+O/fu3cPKyooDBw4oBT8Ahg0bxrBhw554/LVr19KlS5d/tcASQgghhBDiefef\nrSIoHubu7s73338PQFxcnNKLCyAqKorp06cDMG7cOGbOnEnPnj1p164d27dvB+DQoUMMGjRIuWf6\n9OlERUWxdu1akpKS8Pf3p0+fPgDs37+fHj160LVrV4YPH/5QFUMhhBBCCCGKI1lgCUWnTp34+uuv\nSU9P5+zZszRs2DDPa5OSkvjqq69YtmwZ8+bNe+S4ffv2pVKlSqxZs4Z169aRnJxMWFgY//vf/4iO\njubVV1/lf//739N+HCGEEEIIIQqdvCIoFHXr1iUxMZGtW7fi7u7+yGvbt2+PWq2mdu3a3Lp1q0Dz\n/Prrr5w/f55evXoB2T3DGjVq9MRxCyGEEEII8byQBZbIwcPDg08//ZS1a9fy119/5XmdVqt96JiF\nhQVZWVnK7/T09FzvNZvNtG7dmvnz5//7gIUQQgghhHiOyCuCIgc/Pz8++OADnJ2dC3xvtWrVuHDh\nAhkZGdy5c4cffvhBOWdjY6N8Z9WoUSN+/vlnEhISAEhLSyM+Pv7pPIAQQgghhBBFSHawRA5VqlSh\nb9++T3Tviy++SIcOHejcuTMODg688soryrnu3bvz3nvvUalSJdatW8ecOXP48MMPycjIAGDkyJHU\nqFEjz7GzzGaifV2fKC7x/LmXaSzqEIQQQgghngnpgyWKhawsM3/+mVLUYZR4JaUvxfNO8lw4JM+F\nQ/JcOCTPhUPyXDhKSp6lD5Yo3lR5/yUWT9ezzPO9TBOGEvAPVCGEEEKIvMgCSxQLapUK38gjRR2G\n+JcifZthKOoghBBCCCGeoRJR5MJsNtOrVy/27NmjHNu2bRsDBgx4JvONHj0ad3d35fuhmzdv8uab\nbz6TuR7l4MGDDB069KHj48eP548//sBoNNKsWbNCj0sIIYQQQoj/qhKxwFKpVEybNo1PPvmE9PR0\nUlNTWbBgAVOmTPlX4xqNeX+Ir1KpiImJ+VfjPytz5syhZs2aRR2GEEIIIYQQ/zklYoEF4OTkRNu2\nbVmxYgWhoaF4eXnh6OhIdHQ0fn5+eHl5MXXqVKVP06RJk/Dx8cHT05MlS5Yo47i5uREcHIy3tze7\ndu3Kc75+/fqxatUqTCZTjuMpKSn07duXrl27otPp+O677wBISEigc+fOfPTRR7z99tuMGTOGffv2\n0bNnT9566y1OnDgBQGpqKuPGjcPPzw9vb2++/fbbAueiV69enDlzJsex5ORkunfvzt69ewFYvnw5\nfn5+6HQ65flTUlJ477336NKlC507d2b79u15zjF37lw6deqETqfjs88+A7J39nbv3q1c07hxYyB7\np61Pnz4MHjyYdu3asWDBAmJiYvD19UWn05GYmFjgZxRCCCGEEOJ5VKK+wQoMDKRr165otVoiIyM5\nd+4cu3btYuPGjVhaWjJp0iTi4uLQ6XQEBQVRvnx5jEYjffv2pUOHDtSuXRuAChUqPHZ3ysHBgYYN\nGxIbG0vr1q2V46VKlWLp0qXY2try559/0qtXL9q2bQtAfHw8CxcupGbNmnTt2pVSpUqxceNGduzY\nwYoVK1i8eDGhoaG0adOGTz75hL///pvu3bvTunVrSpUq9cR5SUpKYujQoQQFBdGqVSv27NnD1atX\nQUxYtwAAIABJREFUCQ8Px2w28/777/Pzzz9z/fp1qlWrxsqVKwEwGHL/WubWrVvs3buXuLg4VCoV\nd+7ceWwMZ8+e5euvv6ZMmTJ4eHjQq1cvIiMjWb16NevXr2fs2LFP/HxCCCGEEEI8L0rUAsva2ppO\nnTphbW2NVqvl4MGDnDhxAl9fXwDu3btHlSpVAIiLiyMiIgKj0UhSUhLnz59XFlidOnXK13yDBg1i\nxIgRtGrVSjlmNpsJDg7m6NGjqNVqrl27RnJyMgCOjo7KHLVr11buc3JyYtmyZQAcOHCAffv2sXz5\ncgDS09O5evXqI3tEPUpmZiYBAQFMmzZN+R5r//797N27F29vbyC70e/Fixdp2LAhwcHBBAcH07Zt\nW5o2bZrrmOXKlUOtVvPxxx/zxhtv8MYbbzw2jgYNGvDCCy8A8NJLL9GmTRvl2X/55ZcnejYhhBBC\nCCGeNyVqgQWgVqtRq//vzUdfX19GjhyZ45qLFy+ydu1awsPDKVu2LKNHjyY9PV05X7p06XzNVatW\nLWrVqpXjVUK9Xo/BYCA6OhpLS0vc3NyUYhharVa5TqVSKb/VarXyqqHZbCY0NBRHR8cCPnnuLC0t\nqVu3LgcOHFAWWGazmSFDhtCtW7eHro+MjGTPnj3MmzcPNzc3Bg8e/NA1Go2GyMhIDhw4wPbt29mw\nYQOrV6/G0tJSeQXTZDLl+IbtUc/+qG/dhBBCCCGEKE5K3ALrQa1atWL48OH07dsXe3t7bt++zd27\nd0lJScHGxgZbW1uSkpLYv3+/sqNSUEOGDGHIkCFYWman0mAwUKFCBSwtLTlw4AA3btwo0Hiurq6s\nW7eOiRMnAnD69GleeeWVJ4oNshczc+fOZdiwYaxevZr+/fvTpk0bwsLC8PT0xNramuvXr6PVasnM\nzMTOzg5vb29sbW3ZsmVLrmOmpKSQkZFB27Ztady4MR06dACgWrVqnDp1irfeeotdu3Ypi62nIcts\nJtJXKiIWd/cyTY+/SAghhBCiGCvRCyxnZ2cCAwMJCAggKysLjUbD1KlTcXFxoVatWnTs2JGqVavS\npEmTJ56jbt26ODs7c+HCBQC8vLwYPHgwOp0OFxcXqlevXqDxAgMDmT17NjqdjqysLBwdHQkLC8vz\n+v379+Pm5qb8frBgx32WlpYsWrSIgQMHYmNjQ48ePfjjjz/o0aMHADY2NgQHB3PhwgWCg4NRq9Vo\nNBqmTZuW65wpKSkEBgaSkZGB2Wxm3LhxAPTo0YOhQ4fy3Xff0bZt2xy7Vv+aGW7ekg5Kz1pJ6awu\nhBBCCFFUVGaz2VzUQQjxOFlmM2qVqqjDEP/SvUwTBlnAyUK2kEieC4fkuXBInguH5LlwlJQ8V6xY\nJtfjJXoHS5QcapWK7pGnizoM8S9t9n0F2YcUQgghREn2zBdYZrOZ3r17M3jwYNzd3QHYtm0bERER\nrFq16qnPN3r0aA4fPsyuXbvQarXcvHmT3r17P7KnVV4mT57Mr7/+muNYQECAUn3vUQ4ePMiwYcNw\ncHAgIyODLl26MGTIkALHALBnzx7mz5+f49jLL7+MVqulQ4cOtG/fPt9jrV+/njJlytClSxciIiJw\nd3enYsWKeV4/ePBgrl27luPY2LFjef311wv2EEIIIYQQQvwHPPMFlkqlYtq0aYwYMYKWLVtiNBpZ\nsGCB0mvpSRmNRqWwRG5zxsTE0L179381x/Tp0//V/S1atGDp0qWkpqbSpUsX2rZtS926dQs8jru7\nu7I4fdDo0aMLNI7RaOSdd95RfkdGRlK/fv1HLrA+//zzAs0hhBBCCCHEf1mhvCLo5ORE27ZtWbFi\nBWlpaXh5eeHo6Eh0dDTr168nMzOTxo0bM3nyZNRqNZMmTeLUqVOkp6fTsWNHAgMDAXBzc6NLly7s\n37+fQYMG0bFjx1zn69evH6tWrVL6X92XkpLC0KFDMRgMGI1GPvzwQ9q2bUtCQgIffPAB9erV4/jx\n4zRs2BCdTkdoaCjJycnMmzcPFxcXUlNTmTFjBufPn8doNDJ8+HA8PDwe+/w2NjbUr1+fS5cuUb16\ndaZMmcLp06extLRkwoQJNG/enPDwcL7//nv+/vtvkpKS8Pb2ZujQoSQkJDB8+HD0ej0Ay5cvx2g0\nMnTo0BxzLF68mD179pCenk6TJk2YNm0aKpWKXr164eLiwpEjR+jSpQu3b9/Gzs6OSpUq8dtvvzFy\n5EisrKz46KOP2Lx5M4sXLwayd80iIyOV3w8yGo2MHz+e3377DbPZTPfu3enbty+9evVi8uTJ1KtX\nL8fOYXh4OHv27CElJYWEhATef/990tLS2Lp1K1ZWVixfvpyyZcs+/i+SEEIIIYQQzzn14y95OgID\nA4mNjWXfvn28//77nDt3jl27drFx40b0ej0mk4m4uDgAgoKCiIqKQq/Xc/DgQc6fP6+MU6FCBWJi\nYvJcXAE4ODjQsGFDYmNjcxwvVaoUS5cuJTo6mi+++II5c+Yo5+Lj4xk0aBDbtm3j7Nmz7Ny5k40b\nNxIUFMSKFSsACA0NpU2bNkRERLBmzRrmzp2bo39WXpKTkzl+/Di1a9dm7dq1aLVaYmNj+fTTTxkz\nZozSJ+v48eOEhoYSExPD1q1bOXPmTL7z27dvXyIjI4mNjSUlJYW9e/cq57KysoiKiqJfv37KsU6d\nOlG3bl0WLlyIXq/n9ddf5+zZs9y+fRuAqKiohxao9506dYrbt28TGxvL1q1b8/XK5O+//87SpUsJ\nDw8nODiYcuXKERMTQ/369fMsBy+EEEIIIURxU2hFLqytrenUqRPW1tZotVoOHjzIiRMnlH+Jv3fv\nHlWqVAEgLi6OiIgIjEYjSUlJnD9/ntq1awPZC4P8GDRoECNGjKBVq1bKMbPZTHBwMEePHkWtVnPt\n2jWSk5MBcHR0VOaoXbu2cp+TkxPLli0D4MCBA+zbt4/ly5cDkJ6eztWrV6lRo0auMRw6dAhvb2/U\najVDhw6lZs2a/PzzzwwYMACAOnXqUKlSJS5dugRk98AqV64cAO3bt+fo0aP57s/1ww8/sGrVKtLT\n07l9+zb169dXXit81GL0PrVajU6nY+vWreh0Ok6dOvXQd1/3OTo6Eh8fz8yZM3F3d8fV1fWx47ds\n2RJra2vlf23btgWy83vx4sV8PaMQQgghhBDPu0KtIqhWq1Gr/2/TzNfXl5EjR+a45uLFi6xdu5bw\n8HDKli3L6NGjc+wSlS5dOl9z1apVi1q1auUobqHX6zEYDERHR2NpaYmbm5uye/RgzyaVSqX8VqvV\nmEzZzVHNZjOhoaE4OjrmK4b732Dll+ofZchVKhUWFhY5Gvamp6djYWGR47q7d+8yY8YMoqOjqVy5\nMgsWLMiRM2tr63zN7+vry7Bhw4Dshew/57nPzs6OLVu2sHfvXtavX8/OnTuZMWMGlpaWSqz/3Nl7\nML9qtTpHfo1GY77iE0IIIYQQ4nlXZGXaW7VqxfDhw+nbty/29vbcvn2bu3fvkpKSgo2NDba2tiQl\nJbF///587+L805AhQxgyZIhSDMNgMFChQgUsLS05cOAAN27cKNB4rq6urFu3jokTJwJw+vRpXnnl\nlQKN0bRpU2JjY2nevDkXLlzg5s2bODo6cuzYMQ4cOMCdO3fQaDR88803BAcHU7FiRZKSkvj777+x\nsrLi+++/p127djnGvHfvHmq1Gjs7O1JSUti5cyc6ne6xsdjY2JCamqr8fvHFF7Gzs2P58uWsXbs2\nz/uSk5PRarV07NiR6tWrK/moVq0ap06don79+uzYsaNAeXmcLLOZzb4Fy7V4/tzLNBV1CEIIIYQQ\nz1SRLbCcnZ0JDAwkICCArKwsNBoNU6dOxcXFhVq1atGxY0eqVq1KkyZNnniOunXr4uzszIULFwDw\n8vJi8ODB6HQ6XFxcqF69eoHGCwwMZPbs2eh0OrKysnB0dCQsLKxAY/Tp04fJkyej0+mwtLRk7ty5\nym6Oi4sLQ4cOVYpc1KtXD8gule7r60vlypWV1xgfZGdnh7e3N506daJixYo0bNgwX7H4+PgwceJE\nrKysCA8PR6vV0rlzZ1JSUvJ87RHg2rVrTJw4EbPZjEqlUqoZDhgwgFGjRrFhwwbc3NwKlJfHMsPN\nW9JB6VkrKY3/hBBCCCGKispsNpuLOggB4eHhnDt3TtkNKiqTJ0+mcePGdO3atUjj+Kcssxn1P16h\nFMVLeqaJO7J4A2QhW1gkz4VD8lw4JM+FQ/JcOEpKnitWLJPr8SLbwRLPHy8vL8qWLcvHH39c1KE8\nRK1SERB1qajDEP/C/3zy9+2iEEIIIURxVmwXWJMnT+bXX3/NcSwgICBfJcOfxK1bt5gzZw6//PIL\n5cqVQ6PR8N5776HVah+qtvfyyy/n2j/qUbp16/ZEcTVu3Jhjx4490b3/dL/XVp8+fRgzZgwuLi74\n+PgoRT7umzdvXq6vKgohhBBCCPFfV2wXWNOnTy+0ucxmMx988AHe3t7MmzcPgCtXrvDtt9/Sp08f\npRz6kzCZTHlW63seREVFFXUIQgghhBBCFBuF1mi4OPvxxx/RaDT06tVLOVatWjX69OmDyWRi7ty5\n+Pr6otPp2LhxI5DdA6tPnz4MHz6cDh06EBQUxP3P3Tw8PPjss8/o2rUr27dv59KlSwwYMAAfHx96\n9+6tFOXIzeXLl+nRowc6nY4FCxbkOLdy5Uoljgd30PR6PX5+fnh5eTF58mRlR6px48bMnj0bT09P\n/P39lZ5gANu3b8fPz4+3336bI0eOANml18ePH49Op8Pb25sff/wRyF6EPbjgHTRoEIcOHcJkMjFu\n3Dg6d+6MTqfjiy++ACjQ8wohhBBCCFGcyAIrH37//fc8y7FHRERQpkwZIiMjiYyMZPPmzVy+fBnI\nLuM+YcIEvv76axITEzl69KhyX/ny5YmOjsbT05NJkyYxadIkoqKiGDt2LNOmTcszllmzZtGrVy9i\nY2OpVKmScnz//v0kJCQQERGBXq/n1KlTHD58mAsXLrBt2zY2bNiAXq9HrVYTGxsLQFpaGq+++ipx\ncXE0b96cJUuWKOOZTCYiIiKYMGGCcnz9+vUAxMbGMm/ePMaNG/dQv6sHnTlzhhs3brB161ZiY2Px\n8fEBKNDzCiGEEEIIUZwU21cEi9K0adM4evQoGo2GatWqcfbsWaXvk8FgICEhAY1GQ4MGDahSpQqQ\nXTL+ypUrNGvWDMhu5AuQmprKsWPHGDFihDL+/ebHuTl27BghISFAdlGK4OBgAA4cOMCBAweUb9DS\n0tK4ePEiZ8+e5eTJk/j5+QHZPbMqVKgAZDf5vR+Hl5cXgYGByjxvvvkmAPXr1+fKlSsAHD16lHff\nfRfIbuRctWpV4uPj84z1pZde4vLly8yYMQN3d3dcXV0L/LxCCCGEEEIUJ7LAyoc6deqwc+dO5feU\nKVNITk7Gz8+PqlWr8vHHHz/UDPnQoUNKfysACwuLHMUiSpcuDWR/31W2bFmlwER+qHIpV242mxk4\ncCA9e/bMcXzdunV07dqVoKCgAo17P3a1Wv1QkYt/srCwICsrS/l9f1erXLly6PV69u/fz8aNG9m2\nbRsTJ04s8PMKIYQQQghRXMgCKx9atmzJ/Pnz+eqrr+jduzeQvRME4OrqyoYNG2jZsiUajYb4+Hgq\nV66c77FtbW1xcHBg27ZtdOzYEbPZzNmzZ6lbt26u1zdu3Ji4uDi8vLzYsmWLctzV1ZVFixah0+mw\nsbHhxo0bWFpa0qpVK4YOHUq/fv2oUKECf/31F6mpqVSrVo2srCx27NiBp6cnsbGxNG3a9JGxNmvW\njNjYWFq1akV8fDzXrl2jZs2apKSksGHDBrKysrhx4wbHjx8HIDk5Ga1Wy9tvv02NGjX46KOPCvy8\n92WZzVLmu5hLz3z0Ql0IIYQQoiSQBVY+qFQqQkNDmTNnDitXrsTe3p7SpUszevRoOnTowJUrV/Dx\n8cFsNmNnZ8fSpUsLNP5nn33G1KlTCQsLw2g00qlTpzwXHBMnTmT06NGsXLkSDw8P5birqysXLlxQ\ndrCsra357LPPqF27NiNHjqR///5kZWWh0WiYPHky1apVw9ramuPHjxMWFoa9vT0LFy58ZJy9e/dm\n6tSp6HQ6LCwsmDNnDlqtlqZNm1KtWjU6depErVq1qF+/PgBJSUmMHz9e2d368MMPC/y8CjPcvGXI\nVz7Fkyspjf+EEEIIIYqKyny/tJ34z3maPbSetSyzGXUur0aK51dGpom/ZbGWK1nIFg7Jc+GQPBcO\nyXPhkDwXjpKS54oVy+R6XHawRLGgVqmYGn21qMMQBTC1a9WiDkEIIYQQotBJmfbnwO3bt/Hy8sLL\ny4vWrVvTpk0bWrduTYMGDejSpYtyLiwsDIC//vqLDRs2PHZco9GoVC3MTUxMDM7OzkpVQoBbt27x\nyiuvMGvWrEeO/cMPP/DLL7/keX7Xrl2sXLnysTEKIYQQQghRksgO1nPAzs5OqaoXEhKCtbU1AwYM\nyPP6v//+m40bN+ZofPykHB0d+e677xg2bBgA27Zto06dOo+978cff8TOzo5GjRo9dM5oNCpl3oUQ\nQgghhPgvkQXWc27FihXK4qtHjx706dOHefPmER8fj5eXF23atGHw4MEMHToUg8GA0Wjkww8/pG3b\ntvka39rampdeeokzZ85Qr149tm3bRocOHUhOTgayd7SmTp3K1atXUavVfPzxx9jb2xMREYFarSY6\nOpopU6bw1VdfYWNjw6lTp3jttdeoUaMG586dY+LEidy8eZPJkyeTmJiISqVixowZ1KpVi5EjR5KU\nlERWVhaBgYF06NDhmeVRCCGEEEKIwiALrOfYr7/+SmxsLBERERiNRrp168Zrr71GUFAQCQkJysIr\nMzOTpUuXYmtry59//kmvXr3yvcAC8PT0JC4ujjJlymBlZcULL7ygLLBmzpzJe++9R6NGjUhMTGTw\n4MFs3boVPz8/7Ozs6NevHwBfffUVN2/eZPPmzajVasLDw5Xxp0+fTuvWrXn33XcxGo3cu3ePvXv3\nUq1aNeU1QoNBKgQKIYQQQojiTxZYz7GjR4/y1ltvYWVlBUD79u05cuQIrq6uOa4zm80EBwdz9OhR\n1Go1165dIzk5mbJly+ZrHnd3d0JDQylbtiydOnXiwcKSP/zwA/Hx8crvv//+W+kB9k8dOnRArX74\ns76ffvqJ+fPnA2BpaYmtrS3Ozs4EBwcTHBxM27ZtH9uDSwghhBBCiOJAFlglgF6vx2AwEB0djaWl\nJW5ubmRkZOT7/lKlSuHs7MzatWv5+uuv2bFjh3LObDYTHh6OVqt97DjW1tZ5nlP9o8R6rVq1iIyM\nZM+ePcybNw83NzcGDx6c75iFEEIIIYR4HkkVwedYs2bN2L17N/fu3SM1NZVvvvmGZs2aYWNjQ2pq\nqnKdwWCgQoUKWFpacuDAAW7cuFHguQYMGMDo0aMf2vVq1aoVX331lfL7zJkzAA/F8CgtWrRg48aN\nAJhMJlJSUrhx4wY2NjZ4e3vTv39/Tp8+XeCYhRBCCCGEeN7IDtZzrEGDBnh6euLn5wdAr169cHZ2\nBqB+/frodDrc3d0JCAhg8ODB6HQ6XFxcqF69eoHncnZ2VsZ+0JQpU5g6dSqRkZGYTCZatGjBlClT\naNeuHSNHjmTXrl1Mnjz5kWNPmjSJSZMmsWnTJiwsLJg+fTq3b98mODgYtVqNRqNh2rRpjxwjy2yW\nvkrFTEamqahDEEIIIYQodCrzgx/cCPGcysoy8+efKUUdRolXUjqrP+8kz4VD8lw4JM+FQ/JcOCTP\nhaOk5LlixTK5HpcdLFEsqFR5/yUWT9fTyHNGZhZ//5W/V0iFEEIIIUqSYr/AcnZ2JiAggHHjxgGw\natUq0tLSlMa5z8qqVasIDw+nVKlSWFpa0qdPH7y9vZ/pnE/q8OHDBAUFYWdnpxwrXbq08l1UcaBS\nqQiNLvi3ZaJofNC1clGHIIQQQghRJIr9Akur1bJz504GDhyIvb19ocy5YcMGDh48SEREBLa2tqSk\npLBr165CmftJvPjii5QtW1bpmyWEEEIIIYR4Nor9AsvS0pIePXqwZs0aRo0alePct99+S1hYGJmZ\nmZQvX57g4GBeeOEFQkJCSExM5PLly1y7do3x48fzyy+/sG/fPipVqsTnn3+ORqPh5MmTfPLJJ6Sl\npWFnZ8ecOXOoVKkSy5YtY926ddja2gJga2tL165dgey+UXPnzsVkMvHqq68ybdo0tFotHh4eeHp6\nsnfvXiwsLJgxYwbz588nISGBAQMG0KtXLw4dOkRISAhlypTh3LlzdOzYEScnJ9auXUt6ejqhoaE4\nOjqSnJzMlClTuHr1KgATJkygadOmhISEcPXqVRITE7l69Sr+/v707duXefPmcenSJby8vHj99dcJ\nCAhg1KhRpKSkYDKZmDp1Ks2aNcs1v40bN6Znz57s3buXihUr8uGHH/LZZ59x9epVJkyYQLt27TCZ\nTAQHB/PTTz+RkZHBO++8Q8+ePUlNTWXo0KHcuXMHo9HIiBEjaN++PYmJibz//vs0bdqUY8eOUbly\nZZYuXar0+xJCCCGEEKK4KhFl2t955x1iY2MxGAw5jjdt2pTNmzcTExODp6cnK1euVM5dunSJNWvW\nEBYWxkcffUSLFi2IjY3FysqKPXv2kJmZycyZM1m8eDFRUVH4+vqyYMECUlJSSE1N5aWXXnoojvT0\ndMaNG8eCBQuIjY3FZDLlKHH+4osvotfradasGePGjWPRokVs3ryZkJAQ5ZrffvuNadOmsW3bNvR6\nPRcvXiQiIgI/Pz/WrVsHwKxZs/D39ycyMpKQkBA+/vhj5f74+Hjl9cXQ0FAyMzMJCgrC0dERvV7P\n2LFj2bp1K66uruj1evR6PXXr1s0zt2lpabRs2ZK4uDhsbGxYuHAhq1evJjQ0lMWLFwMQERFBmTJl\niIyMJDIyks2bN3P58mVKlSpFaGgo0dHRrFmzhrlz5ypNjBMSEnjnnXeIi4ujTJkyOXpvCSGEEEII\nUVwV+x0syN5B8vLyYu3atTl2Qa5fv86oUaO4efMmGRkZODg4KOfc3NzQaDQ4OTlhMplwc3MDwMnJ\nicTEROLj4zl37hwBAQEAZGVlUbFixUfGER8fj4ODAzVq1ACga9eurF+/nn79+gHQrl07ZY60tDRl\nB0yr1XLnzh0AXFxcqFSpEgCOjo60bt1auefQoUMAHDx4kPPnzyvz3l/0Abi7u6PVarG3t8fe3p4/\n//zzoThdXFyYMGECRqOR9u3bU69evTyfSaPR5MiNVqtV8nblyhUADhw4wNmzZ5VFksFgICEhgSpV\nqjB//nwOHz6MWq3mxo0b3Lp1CwAHBwdl3vr16ytjCSGEEEIIUZyViAUWgL+/Pz4+Pvj4+CjHZs6c\nSb9+/WjXrh2HDh1iyZIlyjmtVgug9GFSqVTKb5PJhNlspk6dOmzatOmhuaytrbl8+XKuu1iPotFo\nlDnuz3//t9FozBHXP6+7HxdkL/Y2b95MqVKlHprjwfstLCyUcR/UvHlzvvzyS/bs2cO4ceMICAjI\ns0DHP3OTWzxms5mPP/6YNm3a5Lg3KiqK5ORkoqKi0Gg0eHh4kJ6enmuc948LIYQQQghRnJWIVwQB\nypcvT4cOHYiIiFCOGQwGKlfOrmYWExNToPFq1KhBcnIyx44dAyAzM5Pff/8dgIEDBzJt2jRSUrL7\nMqWmphITE0ONGjW4cuUKCQkJAOj1epo3b/6vn+2fXF1dldcFAc6cOfPI621sbJQdLoArV67wwgsv\n0L17d7p168apU6f+dTwbNmwgMzMTyN7JS0tLw2AwUKFCBTQaDT/++KPsUgkhhBBCiBKvxOxgAfTv\n35/169crvwMDAxkxYgTlypWjRYsWJCYm5nssrVbL4sWLmTlzJgaDAZPJhL+/P3Xq1KF3796kpaXh\n6+uLRqPB0tKSgIAASpX6f+zde1zO9//48cdVXVdJTaLYtDajskOIyDZyiK0kUZlhVOxjY2jNKQwr\nJSxrq5GZWA4fJOla88vp4zbjY2vjk32WOcxukRxyaEihq6vr90df74+mAxuVPO+3m9vN9T68Xs/3\nU7fdeu71vp4vU6KjowkJCVGaXAwbNuyBP+esWbOIiIjAx8cHvV6Pq6srERERVV7ftGlTOnXqxIAB\nA+jRoweOjo4kJiZiYmKCubk5Cxcu/FvxDBkyhDNnzuDn54fBYKBp06YsXboUHx8fxo0bh4+PDy+9\n9BLPPffcX57DYDBI6+9HSImurK5DEEIIIYSoEyrD7a4DQtRjZWUGLl++XtdhNHgNZWf1+k7yXDsk\nz7VD8lw7JM+1Q/JcOxpKnm1sLCs93qBWsETDpVJV/UMsHqy/mmedrowrV4pqvlAIIYQQogGTAksA\n5a/5lZSUVDi2aNEinJyc6iiiilQqFes2X6zrMEQ1RvhX32VTCCGEEOJxUOtNLgwGA8OGDWPPnj3K\nsYyMDMaMGfNQ5psyZQo9e/ZUioeLFy/Sr1+/hzJXdfbv30/nzp3x9fXF19e3xueNjY3lq6++Asqf\nYdeuXQ81vk2bNin7Yt3+U1+KKyGEEEIIIR4Vtb6CpVKpCA8PJyQkhG7dulFaWkpsbGyFTYD/itLS\nUkxMKn8clUpFWloab7zxxt+a4+9yc3Nj6dKldRpDdarLoRBCCCGEEKJmdfLbtKOjI7179+bLL7+k\nuLgYX19f7O3t2bJlC+vWrUOn0+Hi4sKcOXMwMjJi9uzZHD58mFu3buHl5cWECROA8s2CBw4cyL59\n+3jnnXfw8vKqdL6goCASExPx9/evcPz69euMHz+ewsJCSktL+eCDD+jduzenTp3ivffe4/nnn+e/\n//0vHTp0wMfHhyVLllBQUMDixYtxdnamqKiIefPmceLECUpLS5k0aRJ9+vS5r1ycPn2amTNncuXK\nFZo3b050dDQtW7as8vp9+/bx8ccfU1ZWRocOHZgzZw6HDx/mq6++4rPPPmP79u1Mnz6dn354Xr7J\nAAAgAElEQVT6CZ1Oh6+vLzt37uTkyZPMmzePP/74g0aNGhEZGUnr1q2ZMmUKjRs35vDhw3Tt2pUe\nPXoQHR2NSqXCyMiIdevWYW5uflcc+fn5vP/++xQXF6PX64mIiKB9+/Z069aNAwcOALB161b2799P\nVFQUU6ZMwdLSkuzsbP744w+io6NJSUnh559/plOnTsyfP/++8iaEEEIIIUR9VGfLFRMmTGDw4MFo\nNBo2b97M8ePH2blzJxs2bMDExITZs2ezdetWfHx8mDx5MlZWVpSWljJq1Cg8PT1p27YtAM2aNatx\njys7Ozs6dOhAeno6r776qnLc1NSUpUuXYmFhweXLlxk2bBi9e/cGyvdy+vTTT3nuuecYPHgwpqam\nbNiwge3bt/Pll18SFxfHkiVL6NGjBwsWLODq1au88cYbvPrqq5VuAAyQmZmJr68vAN7e3sp+WkOG\nDGHgwIFs3LiR+fPnExcXV+n9N27cYObMmaxduxZ7e3smT55McnIyb7zxhrKX1YEDB2jTpg2//vor\nxcXFdOzYEYDZs2cTFRWFvb09Bw8eZN68eaxcuRIof20yOTkZIyMj3n77bebNm0eHDh0oKiqq8lm0\nWi29e/dm7Nix6PV6bt68We2/AZTvS7Zp0ya2b9/Ou+++y8aNG5X8Hj9+HEdHxxrHEEIIIYQQoj6r\nswLL3Nyc/v37Y25ujkajYf/+/fzyyy/KKtPNmzeVlZytW7eSkpJCaWkpFy5c4MSJE0qB1b9//3ua\n75133iEkJISXX35ZOWYwGIiJieHgwYMYGRlx7tw5CgoKALC3t1fmaNu2rXKfo6MjX3zxBQD//ve/\n2bt3L8uXLwfg1q1bnD17ltatW1caQ2WvCP73v/9Vxhs0aBCfffZZlc/w+++/8+yzz2Jvb69cn5KS\nwltvvcWTTz7JyZMnyc7OJjAwkJ9++okbN27g6urKtWvX+Pnnn5k4caIyll6vV/7u6emJkVH51/E6\ndepEVFQUPj4+vPbaazRu3LjSWJydnZk7dy4lJSX07duXdu3aUVpaWmXsgLK65+joiK2tbYX8njlz\nRgosIYQQQgjxyKvTL9wYGRkpv9gD+Pv78/7771e45uTJk6xevZpNmzbxxBNPMGXKFG7duqWcb9So\n0T3N1aZNG9q0acPOnTuVY1qtlsLCQrZs2YKJiQnu7u5KMwyNRqNcp1KplM9GRkZKcWIwGFiyZIlS\n8NSlLl268O2332JmZsbLL7/M7NmzuXnzJrNnz1Y2/9VqtZXee+crgOPHj6dPnz7s2bOHoUOH8tVX\nX/Hss8/edc/LL7/MmjVr+Pbbb5k2bRpvv/02AwYM4M5t1e78dwIq5PDP+b2z4BNCCCGEEOJRVW86\nGrz88stMmjSJUaNGYW1tzR9//MGNGze4fv06jRs3xsLCggsXLrBv3z569Ojxl+YYN24c48aNUxo5\nFBYW0qxZM0xMTPj3v/9Nfn7+fY3XvXt31qxZw6xZswD49ddfeeGFF+5rjA4dOpCRkcGAAQP4+uuv\ncXV1rfLaNm3acOrUKU6fPs3TTz/N119/TdeuXQFwdXVl5syZ+Pv7Y2Njw6VLlygoKKBNmzYA2NjY\nsHPnTvr160dZWRnHjx+nXbt2d82Rm5tLu3btaNeuHT///DM5OTmVFlhnzpyhZcuWDB06lBs3bnDk\nyBEGDhxIkyZNOHnyJPb29uzcuRNra+v7ykdVDAaDtAGv53S6sroOQQghhBCiztWbAsvJyYkJEyYQ\nHBxMWVkZarWajz76CGdnZ9q0aYOXlxdPPfUUnTp1+stztGvXDicnJ37//XcAfH19effdd/Hx8cHZ\n2bnSQqI6EyZMYP78+fj4+FBWVoa9vT0JCQn3NcacOXOYOXMmX3zxhdLkoiqNGjUiKiqKCRMmUFZW\nRvv27RkyZAgAHTt25OLFi0qB5uDgQGFhoXJvbGwsH330EfHx8eh0OgYOHFhpgZWYmMjBgwdRqVQ4\nOTlV+M7anb7//nu++uorTExMaNy4MYsWLQLKW8qPGTOGZs2a8eKLL961t9ZfZTDApUuFNV8o/paG\nsrO6EEIIIURdURnufKdLiHrKYDCgUqnqOgxRCZ2ujCtXiuo6jEeKFLK1Q/JcOyTPtUPyXDskz7Wj\noeTZxsay0uP1ZgVLiOqoVCq+3nSprsMQlRg4pHldhyCEEEIIUW80mAJrzpw5/PzzzxWOHT16lODg\nYMLCwoDy19+Ki4srdNN70Pbs2cOsWbO4evUqKpUKlUpF27Zt2bx580Ob8++4du0a6enpjBgxotLz\nR44cUfJ3W6NGjdiwYUNthCeEEEIIIcQjpcEUWBEREXcdc3Z2ZseOHYwdO/aBNVuoydmzZ3FycuKz\nzz7DwsKC69evV+hcWN9cu3aN9evXV1lgPf/881V2HxRCCCGEEEJU1GAKrMqYmJgwdOhQkpKSCA0N\nrXBu9+7dJCQkoNPpsLKyIiYmhubNmxMfH09eXh6nT5/m3LlzzJgxg0OHDrF3715sbW1ZtmwZarWa\n7OxsFixYQHFxMU2bNiU6OhpbW1u++OIL1qxZg4WFBQAWFhYMHjwYKG8MsXDhQvR6PS+99BLh4eFo\nNBr69OmDt7c33333HcbGxsybN49PPvmEU6dOMWbMGIYNG0ZmZibx8fFYWlpy/PhxvLy8cHR0ZPXq\n1dy6dUtpF19QUMDcuXM5e/YsADNnzqRz587Ex8dz9uxZ8vLyOHv2LIGBgYwaNYrFixeTm5uLr68v\nr7zyCsHBwYSGhnL9+nX0ej0fffRRpZ0N9Xo9s2bNIjs7G5VKhb+/P0FBQYwcOZJp06bh7OxMQUEB\nAQEB7N69m9TUVHbt2sWNGzc4deoUo0ePRqfTodVq0Wg0LF++HCsrq4f8EyGEEEIIIcTDZVTzJY+2\nESNGkJ6eXqGjHkDnzp1JTk4mLS0Nb29vVqxYoZzLzc0lKSmJhIQEpk6dipubG+np6ZiZmbFnzx50\nOh2RkZHExcWRmpqKv78/sbGxXL9+naKiIp5++um74rh16xZhYWHExsaSnp6OXq/nn//8p3L+ySef\nRKvV4urqSlhYGJ999hnJycnEx8cr1xw9epTw8HAyMjLQarWcPHmSlJQUAgICWLNmDQBRUVEEBgay\nefNm4uPj+fDDD5X7c3JySExMZNOmTSxZsgSdTsfkyZOxt7dHq9Uyffp0vvnmG7p3745Wq0Wr1Vba\naRDKXx3Mz8/nm2++IT09HT8/vxr/LX777Tfi4+NJSUkhNjYWMzMz0tLS6NixI2lpaTXeL4QQQggh\nRH3XoFewoHwFydfXl9WrV2NmZqYcP3/+PKGhoVy8eJGSkhLs7OyUc+7u7qjVahwdHdHr9bi7uwPg\n6OhIXl4eOTk5HD9+nODgYADKysqwsal+j6acnBzs7Oxo3bo1AIMHD2bdunUEBQUB4OHhocxRXFys\nrIBpNBquXbsGlL/yaGtrC4C9vb3SQt3R0ZHMzEwA9u/fz4kTJ5R5bxd9AD179kSj0WBtbY21tTWX\nL1++K05nZ2dmzpxJaWkpffv25fnnn6/0eZ5++mlOnz7NvHnz6NmzJ927d6/2+QHc3NyU57K0tKRP\nnz5K/MeOHavxfiGEEEIIIeq7Bl9gAQQGBuLn51dhlSUyMpKgoCA8PDzIzMzk888/V85pNBoAjIyM\nUKvVSntwIyMj9Ho9BoMBBwcHNm7ceNdc5ubmykbA90OtVitz3J7/9ufS0tIKcf35uttxQXmxl5yc\njKmp6V1z3Hm/sbGxMu6dunTpwtq1a9mzZw9hYWEEBwczaNCgu65r0qQJWq2Wffv2sWHDBjIyMoiO\njsbY2Jjbnf//vAfWn+O/85lvxy+EEEIIIcSj7LEosKysrPD09CQlJQV/f38ACgsLadGiBcB9v57W\nunVrCgoKyMrKwsXFBZ1Ox8mTJ3FwcGDs2LGEh4fz6aefYmFhQVFRETt37sTLy4szZ85w6tQpnnnm\nGbRaLV26dHngz9q9e3fWrFnD22+/DZS/ylfVKhRA48aNlRUugDNnztCyZUveeOMNSkpKOHz4cKUF\nVkFBARqNhtdff53WrVszdepUAFq1akV2djbt27dn27ZtD+y5DAaDtAOvp3S6sroOQQghhBCi3ngs\nCiyA0aNHs27dOuXzhAkTCAkJoUmTJri5uZGXl3fPY2k0GuLi4oiMjKSwsBC9Xk9gYCAODg4MHz6c\n4uJi/P39UavVmJiYEBwcjKmpKdHR0YSEhChNLoYNG/bAn3PWrFlERETg4+ODXq/H1dW10g6LtzVt\n2pROnToxYMAAevTogaOjI4mJiZiYmGBubs7ChQsrve/ChQvMmDGDsrLyX64/+OADoDzP77//PsnJ\nyfTs2fOBPZfBAJcuFdZ8ofhbGsrGf0IIIYQQdUVluP0+lxD1mMFgUF7VFPWDTlfGlStFNV8o7iKF\nbO2QPNcOyXPtkDzXDslz7Wgoebaxsaz0+GOzgiUebSqVil3/vFjXYYg79B1efWMXIYQQQojHUYNv\n014TJycnFixYoHxOTEys0Br9YQgLC6NPnz74+voqHQ6r06dPHwoKCgBwcXF5qLFVZsiQIUqst//8\nla5///jHP5SOiEIIIYQQQjREj/0KlkajYceOHYwdOxZra+tam3fatGl4enrW2nz3S6/XY2xsDMCm\nTZseyJhffvnlAxlHCCGEEEKI+uqxL7BMTEwYOnQoSUlJhIaGVji3e/duEhIS0Ol0WFlZERMTQ/Pm\nzYmPjycvL4/Tp09z7tw5ZsyYwaFDh9i7dy+2trYsW7YMtVpNdnY2CxYsoLi4mKZNmxIdHa3sY1WZ\nb775hi+++AKDwUDPnj2VznyVMRgMLFq0iL1796JSqRg3bhz9+/cnPDyc7t274+HhwXvvvccTTzxB\ndHQ0KSkpnD59mtDQULRaLWvWrEGn09GhQwfmzp2LsbExLi4uDB06lP379zNnzhy+/fZbdu/ejbGx\nMd27d2f69OmVxhIWFoapqSlHjhzh8uXLzJ8/n7S0NA4dOkSHDh2UFcI+ffqQkpJCcXEx//jHP+jc\nuTNZWVm0aNGCpUuXVtinTAghhBBCiEfRY/+KIMCIESNIT0+nsLBil7rOnTuTnJxMWloa3t7erFix\nQjmXm5tLUlISCQkJTJ06FTc3N9LT0zEzM2PPnj3odDoiIyOJi4sjNTUVf39/YmNjlfsXLVpU4XW7\n/Px8YmJiSEpKIi0tjV9++YVdu3ZVGfOOHTs4evQoWq2WVatWsWjRIi5cuICrqysHDhwAID8/n99/\n/x2AgwcP4urqyu+//05GRgbr169Hq9ViZGREeno6AMXFxbRv356vv/6aNm3asHPnTrZu3Up6ejrj\nxo2rNofXrl1j48aNzJgxg3HjxhEUFMTWrVs5fvw4R44cuev6U6dOMWLECLZu3YqlpSXbt2+v4V9J\nCCGEEEKI+u+xX8ECsLCwUL4Ldecqyvnz5wkNDeXixYuUlJRgZ2ennHN3d0etVuPo6Iher8fd3R0A\nR0dH8vLyyMnJ4fjx4wQHBwPlGwDb2PyvKcCfXxHctWsXXbt2VV5T9PHx4aeffqJv376Vxnzw4EG8\nvb0xNjamefPmdOnShV9++QVXV1eSkpI4ceIEbdu25erVq1y4cIGsrCxmzZpFWloa2dnZBAQEAHDz\n5k2aNWsGlG8+/PrrrwNgaWmJqakpM2fOpHfv3vTq1avaHPbu3RuVSoWTkxPNmzfHyckJgLZt23Lm\nzJm79uKys7NTjr344oucOXOm2vGFEEIIIYR4FEiB9X8CAwPx8/PDz89PORYZGUlQUBAeHh5kZmby\n+eefK+c0Gg0ARkZGqNVqpYW4kZERer0eg8GAg4MDGzdurNXnaNGiBdeuXWPv3r24urpy9epVMjIy\nMDc3x8LCAoPBwODBg5k8efJd95qamirfuzIxMSElJYXvv/+ebdu2sXbt2mqbcdzOh0qlUv4O5fko\nLS2t8nooL+xu3br1l59ZCCGEEEKI+kIKrP9jZWWFp6cnKSkp+Pv7A1BYWEiLFi0ASEtLu6/xWrdu\nTUFBAVlZWbi4uKDT6Th58iQODg6VXt++fXuioqIoKCigSZMmbN26lbfeeqvK8V1dXdm4cSODBw/m\n6tWrHDhwgGnTpgHQsWNHkpKSSEpK4sqVK0yaNElZmXr55ZcZP348QUFBNGvWjCtXrlBUVESrVq0q\njF9UVMTNmzfp2bMnnTp1qnIlrbYYDAZpC17P6HRldR2CEEIIIUS9IwXWHUaPHs26deuUzxMmTCAk\nJIQmTZrg5uZGXl7ePY+l0WiIi4sjMjKSwsJC9Ho9gYGBVRZYtra2TJ48mcDAQKXJRXVFTb9+/cjK\nysLX1xeVSsXUqVOVVxA7d+7Mvn37eOaZZ3jqqae4evUqrq6uQPkre++//z6jR4+mrKwMtVrNnDlz\nKi2wxo8fr6wshYWF3fOzPwwGA1y6VFjzheJvaSgb/wkhhBBC1BWVwWAw1HUQQtTEYDAor2GKuleq\nK+OPK0V1HcYjSwrZ2iF5rh2S59ohea4dkufa0VDybGNjWelxWcESjwSVSsX+1RfrOgzxf14ZJa9r\nCiGEEEJUpsEWWE5OTgQHByuvtiUmJlJcXMzEiRMf6ryJiYls2rQJU1NTTExMGDlyJIMGDXqoc/5V\n165dIz09nREjRtzT9QkJCWzbtq3CMU9PzxpbuAshhBBCCPG4aLAFlkajYceOHYwdO1Zpff6wrV+/\nnv3795OSkoKFhQXXr19n586dtTL3X3Ht2jXWr19/zwXWuHHjpJgSQgghhBCiGg22wDIxMWHo0KEk\nJSURGhpa4dzu3btJSEhAp9NhZWVFTEwMzZs3Jz4+nry8PE6fPs25c+eYMWMGhw4dYu/evdja2rJs\n2TLUajXZ2dksWLCA4uJimjZtSnR0NLa2tnzxxResWbMGCwsLoHx/rcGDBwPw/fffs3DhQvR6PS+9\n9BLh4eFoNBr69OmDt7c33333HcbGxsybN49PPvmEU6dOMWbMGIYNG0ZmZibx8fFYWlpy/PhxvLy8\ncHR0ZPXq1dy6dYslS5Zgb29PQUEBc+fO5ezZswDMnDmTzp07Ex8fz9mzZ8nLy+Ps2bMEBgYyatQo\nFi9eTG5uLr6+vrzyyisEBwcTGhrK9evX0ev1fPTRR0pzjD9zcXHhzTff5LvvvsPGxoYPPviAjz/+\nmLNnzzJz5kw8PDzIy8tj2rRp3LhxA4DZs2fTqVMndu7cydq1a/nqq6+4ePEiI0eOZO3atRX2CRNC\nCCGEEOJRZFTXATxMI0aMID09ncLCit3nOnfuTHJyMmlpaXh7e7NixQrlXG5uLklJSSQkJDB16lTc\n3NxIT0/HzMyMPXv2oNPpiIyMJC4ujtTUVPz9/YmNjeX69esUFRXx9NNP3xXHrVu3CAsLIzY2lvT0\ndPR6Pf/85z+V808++SRarRZXV1fCwsL47LPPSE5OJj4+Xrnm6NGjhIeHk5GRgVar5eTJk6SkpBAQ\nEMCaNWsAiIqKIjAwkM2bNxMfH8+HH36o3J+Tk6O8vrhkyRJ0Oh2TJ0/G3t4erVbL9OnT+eabb+je\nvTtarRatVku7du2qzG1xcTHdunVj69atNG7cmE8//ZSVK1eyZMkS4uLiAGjWrBmrVq1iy5YtxMbG\nEhkZCZR3QLSxsWHdunXMnj2biRMnSnElhBBCCCEahAa7ggXlK0i+vr6sXr0aMzMz5fj58+cJDQ3l\n4sWLlJSUYGdnp5xzd3dHrVbj6OiIXq/H3d0dAEdHR/Ly8sjJyeH48eMEBwcDUFZWVmNxkJOTg52d\nHa1btwZg8ODBrFu3jqCgIAA8PDyUOYqLi5UVMI1Gw7Vr1wBwdnbG1tYWAHt7e1599VXlnszMTAD2\n79/PiRMnlHlvF30APXv2RKPRYG1tjbW1NZcvX74rTmdnZ2bOnElpaSl9+/bl+eefr/KZ1Gp1hdxo\nNBolb2fOnAGgtLSUiIgIjh49ipGRESdPnlTunz17NgMGDKBjx44MGDCg2vwJIYQQQgjxqGjQBRZA\nYGAgfn5++Pn5KcciIyMJCgrCw8ODzMxMPv/8c+WcRqMBwMjICLVarbQGNzIyQq/XYzAYcHBwYOPG\njXfNZW5uzunTpytdxaqOWq1W5rg9/+3PpaWlFeL683W344LyYi85ORlTU9O75rjzfmNjY2XcO3Xp\n0oW1a9eyZ88ewsLCCA4OrrJBx59zU1k8X331Fc2bN0er1VJWVkb79u2V+8+fP4+RkRGXLl2irKwM\nI6MGvZgqhBBCCCEeEw2+wLKyssLT05OUlBT8/f0BKCwspEWLFgCkpaXd13itW7emoKCArKwsXFxc\n0Ol0nDx5EgcHB8aOHUt4eDiffvopFhYWFBUVsXPnTry8vDhz5gynTp3imWeeQavV0qVLlwf+rN27\nd2fNmjW8/fbbABw5cqTaVajGjRsrK1wAZ86coWXLlrzxxhuUlJRw+PDhv9UBsbCwkJYtW2JkZMSW\nLVuUwqu0tJSZM2eyePFi0tLSWLVqFWPGjKl2LIPBIK3B65FSXVldhyCEEEIIUS81+AILYPTo0axb\nt075PGHCBEJCQmjSpAlubm7k5eXd81gajYa4uDgiIyMpLCxEr9cTGBiIg4MDw4cPp7i4GH9/f9Rq\nNSYmJgQHB2Nqakp0dDQhISFKk4thw4Y98OecNWsWERER+Pj4oNfrcXV1JSIiosrrmzZtSqdOnRgw\nYAA9evTA0dGRxMRETExMMDc3Z+HChX8rnuHDhzNx4kTS0tLo0aMH5ubmACxbtgxXV1dcXV1p164d\nAQEB9OrVizZt2lQ5lsEAly4VVnlePBgNZeM/IYQQQoi6ojIYDIa6DkKImhgMBuWVRFH3SkvK+ONq\nUc0XikpJIVs7JM+1Q/JcOyTPtUPyXDsaSp5tbCwrPf5YrGCJR59KpSJrxYW6DkP8H5e3bes6BCGE\nEEKIeqlBdha4dOkSkydPxsPDAz8/P4YOHVqvNvxNTU1VXt3btWtXhc5/D9OyZcsqfH7zzTdrvGfI\nkCH4+vpW+HPs2LG/FUdYWBjbtm37W2MIIYQQQghRHzW4FSyDwcB7773HoEGDWLx4MVDevGH37t1/\ne2y9Xo+xsfHfHudOu3btolevXrRt2/ae7yktLcXE5P7/6b744gveffdd5fOGDRtqvGfTpk0PJRYh\nhBBCCCEaogb3m/EPP/yAWq2u0ESiVatWjBw5Er1eT0xMDD/++CMlJSWMGDGCN998U2nV3rRpU44f\nP86LL75ITEwMKpWKPn364OXlxf79+3n77bdxdnYmPDycP/74AzMzM+bNm1dlc4bdu3eTkJCATqfD\nysqKmJgYmjdvrpz/z3/+w+7du/nxxx9JSEhQNhaubPywsDA0Gg1HjhyhU6dOWFhYcPbsWfLy8jh7\n9iyBgYGMGjUKgPHjx3P+/Hlu3brFqFGjGDp0KDExMdy8eRNfX1/atm3L4sWLcXFxISsri9DQUHx9\nfenVqxdQvsLUq1cv+vXrV2W+PvvsM5544glycnLYvn07q1atYvPmzQAEBAQoe3ylpaWRmJiISqXC\nycmJjz/+uEKOPv30U86fP09UVNQDL16FEEIIIYSobQ2uwPrtt9944YUXKj2XkpKCpaUlmzdvpqSk\nhDfffFPZsPfXX39l69at2NraMmzYMA4ePIirqytQ3up9y5YtQPm+WuHh4Tz77LP8/PPPhIeHs3r1\n6krn69y5M8nJyahUKjZt2sSKFSsICwtTznfq1Ik+ffrQq1cvPD09axw/Pz+fDRs2YGxsTHx8PDk5\nOaxevZrr16/j5eXFsGHDUKvVzJ8/HysrK27evElAQACvvfYaU6ZMYd26dWi12rvi7N+/PxkZGfTq\n1YuSkhK+//57PvrooxrzlZ6eztNPP012djapqakkJydjMBh444036Nq1K2q1moSEBNavX4+1tTVX\nrlypMO/ChQspKioiOjpaGlgIIYQQQogGocEVWH8WHh7OwYMHUavVtGrVimPHjrF9+3agfJ+mU6dO\noVarad++PS1btgSgXbt2nDlzRimw+vfvD0BRURFZWVmEhIQo45eUlFQ59/nz5wkNDeXixYuUlJRg\nZ2dXbaw1je/p6Vlhladnz55oNBqsra2xtrbm8uXLtGzZkjVr1ijfOTt37hynTp2iadOmVc7r7u5O\nVFQUJSUlfPfdd7i6umJmZsa///3vKvPl7OysbKh88OBB+vbtq7Rh79evHwcOHEClUuHp6Ym1tTVQ\nXqjetnTpUjp06MC8efOqzYkQQgghhBCPkgZXYDk4OLBjxw7l89y5cykoKCAgIICnnnqKDz/8kB49\nelS4JzMzE41Go3w2NjZWNsUFaNSoEVD+/a4nnnii0lWgykRGRhIUFISHh4fyGmJ1ahr/dhy3/Tnm\n0tJSMjMz2b9/Pxs3bqRRo0aMHDmSW7duVTuvqakpXbt2Ze/evWRkZCgFpcFgqDJft4upv8rZ2ZnD\nhw9z5cqVCoWXEEIIIYQQj7IGV2B169aNTz75hH/+858MHz4cgJs3bwLQvXt31q9fT7du3VCr1eTk\n5NCiRYt7HtvCwgI7OzsyMjLw8vLCYDBw7Ngx2rVrV+n1hYWFyvhpaWmVXtO4cWOKior+0vhVzdmk\nSRMaNWrE77//zqFDh5RzJiYm6HQ61Gr1Xff179+fTZs2kZ2dTXR0NHDv+XJ1dSUsLIyxY8diMBjY\ntWsXixYtQq1WM2HCBIKCgmjatGmFYqpHjx50796dd955h8TERCwsLKp9LoPBIK3B65HSkrK6DkEI\nIYQQol5qcAWWSqViyZIlREdHs2LFCqytrWnUqBFTpkzB09OTM2fO4Ofnh8FgoGnTpixduvS+xv/4\n44/56KOPSEhIoLS0lP79+1dZAE2YMIGQkBCaNGmCm5sbeXl5d13Tv39/Zs+ezZo1a4iLi7uv8Svj\n7u7Ohg0b8PLyonXr1nTs2FE598YbbzBw4EBeeOEFpcPiba+++irTpk3Dw8NDWRkbMk/zg+sAACAA\nSURBVGTIPeXrxRdfxM/PjyFDhgDlTS5ufw/u3XffZeTIkRgZGfHCCy+wYMEC5T4vLy+KiooYN24c\nX375JWZmZlU+l8EAly4V3nMexF/TUDb+E0IIIYSoKyqDwWCo6yCEqInBYJBGGPVEaUkZf1wtqusw\nHmlSyNYOyXPtkDzXDslz7ZA8146GkmcbG8tKjze4FSzRMKlUKo4tya/rMATg9N69v1YrhBBCCPG4\nMarrABqChIQEfH19lT9OTk4V9uFKTExU9rh6WMLCwti2bVuFY/n5+UyaNAmA1NRUIiIiHmoM1Vm/\nfn2V30MTQgghhBCioZAVrAdg3LhxjBs3Tvns7OxMfn4+BQUFSovyutCiRQvi4uLqbP473VlwCiGE\nEEII0VBJgfUQmJiYMHToUJKSkggNDa1wbvfu3SQkJKDT6bCysiImJobmzZsTHx9PXl4ep0+f5ty5\nc8yYMYNDhw6xd+9ebG1tWbZsGWq1muzsbBYsWEBxcTFNmzYlOjoaW9vKu+vl5eXx7rvv8s0331Q4\n/u2335KQkEBCQgJQ3sr+7NmzAMycOZPOnTvz448/EhUVBZS/nrd27dpKO/1lZmYSHx+PpaUlx48f\nx8vLC0dHR1avXs2tW7dYsmQJ9vb2xMfHY25uzpgxYxg5ciTt27cnMzOTwsJCoqKilD3HhBBCCCGE\neJTJK4IPyYgRI0hPT6ewsGLnu86dO5OcnExaWhre3t6sWLFCOZebm0tSUhIJCQlMnToVNzc30tPT\nMTMzY8+ePeh0OiIjI4mLiyM1NRV/f39iY2PvK66dO3eyfPlyli9fjrW1NVFRUQQGBrJ582bi4+P5\n8MMPAVi5ciVz5sxBq9Wybt26ajv8HT16lPDwcDIyMtBqtZw8eZKUlBQCAgJYs2ZNpffo9XpSUlKY\nOXNmjfuDCSGEEEII8aiQFayHxMLCAl9fX1avXl2hODl//jyhoaFcvHiRkpIS7OzslHPu7u6o1Woc\nHR3R6/W4u7sD4OjoSF5eHjk5ORw/fpzg4GAAysrKsLGxueeYfvjhB7Kzs1m5cqWyGrV//35OnDih\nXHP9+nWKioro1KkTCxYswMfHh9dee43GjRtXOa6zs7OyimZvb8+rr76qxJ2ZmVnpPf369QPKW7yf\nOXPmnp9BCCGEEEKI+kwKrIcoMDAQPz8//Pz8lGORkZEEBQXh4eFBZmZmhdWb2/tPGRkZoVarlbbk\nRkZG6PV6DAYDDg4ObNy48S/FY29vz+nTp8nJycHZ2RkoL9KSk5MxNTWtcO3YsWPp2bMne/bsYdiw\nYaxYsYI2bdpUOu7tuG/Heudz6PX6au+p7hohhBBCCCEeNfKK4ENkZWWFp6cnKSkpyrHCwkJatChv\nc32/XfVat25NQUEBWVlZAOh0On777bd7vv+pp54iLi6O6dOnK/d17969wmt8R44cAcpfV3RycmLs\n2LE4OzuTk5NzX7EKIYQQQgjxOJIVrIds9OjRrFu3Tvk8YcIEQkJCaNKkCW5ubuTl5d3zWBqNhri4\nOCIjIyksLESv1xMYGIiDgwNQ3qxi/vz5ADz55JMsXrz4rjHatGlDTEwMISEhLFu2jFmzZhEREYGP\njw96vR5XV1ciIiJISkoiMzMTlUqFg4OD8rpiXTEYDLL/Uj1RWlJW1yEIIYQQQtRbKoPBYKjrIISo\nSVmZgcuXr9d1GA1eQ9lZvb6TPNcOyXPtkDzXDslz7ZA8146GkmcbG8tKj8sKlngkqKj6h1g8WDXl\nWV9SRsHVolqKRgghhBDi0dKgCiwnJyeCg4MJCwsDIDExkeLiYiZOnPjQ5gwLC+PHH3/E0tISIyMj\n5syZg4uLywOdw8XFRfne1b36xz/+obwimJ6ezogRI/5WDMeOHWPatGkVjmk0GjZt2vS3xr1XKiMV\npxefr5W5RPWentyyrkMQQgghhKi3GlSBpdFo2LFjB2PHjsXa2rrW5p02bRqenp7s27ePOXPmkJ6e\nXmtz/5nBYMBgMPDll18C5ZsNr1+//m8XWE5OTmi12gcRohBCCCGEEA1WgyqwTExMGDp0KElJSYSG\nhlY4t3v3bhISEtDpdFhZWRETE0Pz5s2Jj48nLy+P06dPc+7cOWbMmMGhQ4fYu3cvtra2LFu2DLVa\nTXZ2NgsWLKC4uJimTZsSHR2t7P10W5cuXcjNzQXKu/HNnTuXGzduYG9vz/z582nSpAkjR47EycmJ\nn376Cb1ez/z582nfvj3x8fGYm5szZswYAAYMGMCyZcsq7JNVVFTE+PHjuXbtGqWlpYSEhNC3b1/y\n8vIYM2YMHTp04PDhwyxfvpyRI0eSkpLC4sWLyc3NxdfXl1deeYXLly/z2muv0bdvXwAmT56Ml5eX\n8vlOqamp7Nq1ixs3bnDq1ClGjx6NTqdDq9Wi0WhYvnw5VlZW5ObmEh4ezh9//IGZmRnz5s2jTZs2\n1eb87Nmz5OXlcfbsWQIDAxk1atQD/VkQQgghhBCiLjS4Nu0jRowgPT2dwsLCCsc7d+5McnIyaWlp\neHt7s2LFCuVcbm4uSUlJJCQkMHXqVNzc3EhPT8fMzIw9e/ag0+mIjIwkLi6O1NRU/P39iY2NvWvu\n3bt34+joCJSvak2ZMoX09HQcHR0r7Hd18+ZNtFotc+fOZebMmff8bKampixZsoQtW7aQlJTEwoUL\nud2j5NSpUwwfPpytW7fSqlUr5Z7Jkydjb2+PVqtl+vTpBAQEkJqaCpS3jM/KyqJXr15Vzvnbb78R\nHx9PSkoKsbGxmJmZkZaWRseOHZU287Nnz2b27NmkpqYyffp0wsPDa8x5Tk4OiYmJbNq0iSVLlqDT\n6e45D0IIIYQQQtRXDWoFC8DCwgJfX19Wr16NmZmZcvz8+fOEhoZy8eJFSkpKKqwMubu7o1arcXR0\nRK/XKy3JHR0dycvLIycnh+PHjxMcHAyUb85rY2Oj3L9o0SISEhKwtrYmKiqKwsJCCgsL6dq1KwCD\nBw8mJCREud7b2xsoX/G6fv06165du6dnMxgMfPLJJ/z0008YGRmRn5/PpUuXgPI9rjp27FjjGF27\ndiU8PJyCggK2b9/O66+/jolJ1T8Gbm5uWFhYAGBpaUmfPn2U3Bw7doyioiKysrIqPF9JSQlQfc57\n9uyJRqPB2toaa2trLl++TMuW8t0eIYQQQgjxaGtwBRZAYGAgfn5++Pn5KcciIyMJCgrCw8ODzMzM\nCitKGo0GACMjI9RqNSqVSvms1+sxGAw4ODiwcePGSue7/R2s2/68evZnt8e/87OxsTFlZf/bX+jW\nrVt33Zeenk5BQQGpqamo1Wr69OmjXGdubl7tnHfy9fXl66+/ZuvWrURHR1d77e3cwP/yc/vvt3Pz\nxBNPVPr9rHvJOYCxsTGlpaX3HL8QQgghhBD1VYN7RRDAysoKT09PUlJSlGOFhYW0aFG+Ue3tV9vu\nVevWrSkoKFA6+el0On777bcqr7e0tOSJJ57gwIEDAGi1Wrp06aKc/3//7/8BcODAASwtLbG0tKRV\nq1b8+uuvABw+fLjSDYgLCwtp1qwZarWaH374gTNnztQYe+PGjSkqqthS28/Pj6SkJADatm1b4xjV\nsbCwwM7OjoyMDKB8le3o0aNKvH8150IIIYQQQjyKGuQKFsDo0aNZt26d8nnChAmEhITQpEkT3Nzc\nKi1gqqLRaIiLiyMyMpLCwkL0ej2BgYE4ODhUec/ChQuVJhdPP/10hZUiU1NTBg0aRGlpKfPnzwfg\n9ddfR6vV4u3tTfv27Xn22WfvGtPHx4dx48bh4+PDSy+9xHPPPVdj7E2bNqVTp04MGDCAHj16MH36\ndJo3b85zzz1XaWOLv+Ljjz/mo48+IiEhgdLSUvr370+7du3+Vs7/zFBmkPbg9YS+pKzmi4QQQggh\nHlMqw+0uCaJWjBw5kmnTpuHs7FxnMdy4cQMfHx+2bNmCpeWjsXlvWZmBy5ev13UYDV5D2Vm9vpM8\n1w7Jc+2QPNcOyXPtkDzXjoaSZxubyn+PbrArWKJy+/fvZ9asWQQGBj4yxRWAiqp/iMWDVV2e9SV6\nCq4++v9BFEIIIYR4WGQFS7B3715iYmIqHLOzs2PJkiV1FFHlzn98sq5DeOy1nPosFy9W38RF1Kyh\n/J+7+k7yXDskz7VD8lw7JM+1o6HkWVaw7uDk5ERwcDBhYWEAJCYmUlxczMSJEx/anGFhYfz444/K\nqpG/v3+1m+v26dOHlJQUrK2tcXFxURpsPAw9evSgR48eD218IYQQQgghHhePZYGl0WjYsWMHY8eO\nxdrautbm/XM79/pGr9djbGxc12EIIYQQQgjxyGqQbdprYmJiwtChQ5VW5XfavXs3Q4YMYdCgQQQF\nBSkb+cbHxzN9+nSGDx9O79692bFjB4sWLcLHx4cxY8ag0+kAyM7O5q233sLPz48xY8Zw4cKFamP5\n5ptv8PHxYcCAAXz88cfVXmswGFi4cCEDBgzAx8dHafceHh7Ov/71LwDee+89ZsyYAUBKSgqxsbFA\neav4gIAAfH19mTNnDnq9HgAXFxcWLFjAwIEDycrKIiYmhv79++Pj48PChQurjCUjI4MBAwYwcOBA\nRowYAUBqaioRERHKNe+88w6ZmZnKPAsXLsTb25ugoCD++9//MnLkSDw8PJTYhRBCCCGEeNQ9lgUW\nwIgRI0hPT79rU+DOnTuTnJxMWloa3t7erFixQjmXm5tLUlISCQkJTJ06FTc3N9LT0zEzM2PPnj3o\ndDoiIyOJi4sjNTUVf39/pcABWLRoEb6+vvj6+nLs2DHy8/OJiYkhKSmJtLQ0fvnlF3bt2lVlzDt2\n7ODo0aNotVpWrVrFokWLuHDhAq6ursqeW/n5+fz+++8AHDx4EFdXV37//XcyMjJYv349Wq0WIyMj\n0tPTASguLqZ9+/Z8/fXXtGnThp07d7J161bS09MZN25clbEsXbqUxMREvv76axISEmrMd3FxMd26\ndWPr1q00btyYTz/9lJUrV7JkyRLi4uJqvF8IIYQQQohHwWP5iiCUb5Dr6+vL6tWrMTMzU46fP3+e\n0NBQLl68SElJCXZ2dso5d3d31Go1jo6O6PV63N3dAXB0dCQvL4+cnByOHz9OcHAwAGVlZdjY2Cj3\n//kVwV27dtG1a1flNUUfHx9++umnKvenOnjwIN7e3hgbG9O8eXO6dOnCL7/8gqurK0lJSZw4cYK2\nbdty9epVLly4QFZWFrNmzSItLY3s7GwCAgIAuHnzJs2aNQPA2NiY119/HSjfINnU1JSZM2fSu3dv\nevXqVWX+XFxcCAsLw8vLi379+tWYb7VaXSFfGo1GyeW9bJgshBBCCCHEo+CxLbAAAgMD8fPzw8/P\nTzkWGRlJUFAQHh4eZGZm8vnnnyvnNBoNAEZGRqjValQqlfJZr9djMBhwcHBg48aNtfocLVq04Nq1\na+zduxdXV1euXr1KRkYG5ubmWFhYYDAYGDx4MJMnT77rXlNTU+V7VyYmJqSkpPD999+zbds21q5d\ny+rVqyudMyIigp9//plvv/0Wf39/Nm/ejLGxMWVl/9uE9tatW8rf/5yvO3N5+3VFIYQQQgghHnWP\ndYFlZWWFp6cnKSkp+Pv7A1BYWEiLFi0ASEtLu6/xWrduTUFBAVlZWbi4uKDT6Th58iQODg6VXt++\nfXuioqIoKCigSZMmbN26lbfeeqvK8V1dXdm4cSODBw/m6tWrHDhwgGnTpgHQsWNHkpKSSEpK4sqV\nK0yaNElZmXr55ZcZP348QUFBNGvWjCtXrlBUVESrVq0qjF9UVMTNmzfp2bMnnTp1qnIlDcpfl+zQ\noQMdOnTgu+++4/z587Rq1Yr169dTVlZGfn4+//3vf+8rf9UxlBloOfXZBzae+Gv0JVIMCyGEEEJU\n57EusABGjx7NunXrlM8TJkwgJCSEJk2a4ObmRl5e3j2PpdFoiIuLIzIyksLCQvR6PYGBgVUWWLa2\ntkyePJnAwEAMBgM9e/astqjp168fWVlZ+Pr6olKpmDp1qvIKYufOndm3bx/PPPMMTz31FFevXsXV\n1RWAtm3b8v777zN69GjKyspQq9XMmTOn0gJr/PjxysrT7Tb2lVm0aBGnTp3CYDDQrVs32rVrB0Cr\nVq3o378/bdq04cUXX7zn3NXEAFyS/ZceuoayL4UQQgghRF2RjYbFI8FQZkBlpKrrMB57+hI9BVel\nAPu7pJCtHZLn2iF5rh2S59ohea4dDSXPstGwUIwcOZJp06bh7Ox8T9dnZGQQFxdH8+bNWbNmzUOO\nrnIqIxXnP/m1TuYW/9PygxfqOgQhhBBCiHrtsW3TLu5NQkICs2bNorS0lGvXruHr63tPbdnvR2lp\n6QMdTwghhBBCiLoiBdYjYMWKFUo3v/nz5zNq1CgAvv/+eyZPnsy+ffsYOnQogwcPZtKkSRQVFQE1\nb3pcVlZGWFiYsldXZZse3+6OaGRkxCuvvIKFhUWF9u3Dhg3j6NGjFBcXM2PGDAICAhg0aJCyn1de\nXh7Dhw9n8ODBDB48mP/85z8AZGZmMnz4cN599128vb0fXvKEEEIIIYSoRVJgPQLu3Eg4Ozub4uJi\ndDodBw8exMnJiYSEBFatWsWWLVt46aWXWLVqVY2bHuv1eqZMmcIzzzxDaGholZseT5gwgZdeeomY\nmBimT59OQEAAqampAOTk5HDr1i3atWvHsmXL6NatGykpKaxevZqPP/6Y4uJimjVrpsQWGxtLZGSk\nEsOvv/7KrFmz2L59e+0mVAghhBBCiIdEvoP1CHjxxRc5fPgw169fR6PR8MILL5Cdnc2BAwfo06cP\nJ06cYNiwYQDodDo6duxY46bHc+bMwcvLi3HjxgHwyy+/3NOmx56enixdupRp06axefNmZQ+xffv2\nsXv3blauXAmU74F17tw5bG1tiYiI4OjRoxgZGXHy5EllLGdnZ55++umHkzQhhBBCCCHqgBRYjwC1\nWo2dnR2pqam4uLjg5OREZmYmubm52NnZ8eqrr/LJJ59UuOfYsWPVbnrs4uJCZmYmo0ePxtTU9J5j\nadSoEa+88gr/+te/yMjIUFazAOLi4njuuecqXB8fH0/z5s3RarWUlZXRvn175Zy5ufk9zyuEEEII\nIcSjQF4RfES4urqycuVKunTpgqurKxs2bOD555+nY8eO/Oc//+HUqVMAFBcXk5OTU2HTYyhf2frt\nt9+U8QICAujZsychISGUlpbSvn17fvrpJwoKCtDr9WzdupUuXbpUGsuQIUOIjIzE2dmZJk2aANC9\ne3fWrl3L7a7/v/5a3vGvsLAQGxsbjIyM0Gq16PWyUa0QQgghhGi4ZAXrEeHq6sqyZcvo2LEj5ubm\nmJqa4urqirW1NdHR0XzwwQeUlJQA8P7779O6desaNz0ODg6msLCQadOmERMTc8+bHr/00ktYWFgo\nrwcCjB8/nvnz5zNw4EDKysqws7Pjiy++YPjw4UycOJG0tDR69Ojxl1etDGUGaRFeD+hLpEAWQggh\nhKiObDQs7lt+fj6jRo0iIyMDI6PaWQQtKzNw+fL1WpnrcdZQNv6r7yTPtUPyXDskz7VD8lw7JM+1\no6HkWTYaFg9EWloasbGxhIWF1VpxBaBSVf1DLB6sqvKsL9FTcPXR/4+hEEIIIcTDJAWWuC+DBg1i\n0KBBtT6vSqUi/9ODtT6v+J8W73eu6xCEEEIIIeo9aXLxgDg5ObFgwQLlc2JiIvHx8Q91zrCwMLZt\n21bhWH5+PpMmTQIgNTWViIiIhxqDEEIIIYQQ4n+kwHpANBoNO3bsoKCgoE7jaNGiBXFxcXUagxBC\nCCGEEI8rKbAeEBMTE4YOHUpSUtJd53bv3s2QIUMYNGgQQUFBXLp0CSjfI2r69OkMHz6c3r17s2PH\nDhYtWoSPjw9jxoxBp9MBkJ2dzVtvvYWfnx9jxozhwoULVcaRl5fHgAED7jr+7bffMnToUAoKCigo\nKGDixIn4+/vj7+/PwYPlr979+OOP+Pr64uvry6BBg7h+vfKmEpmZmbz11luMGzcODw8PYmJi+Prr\nrwkICMDHx4fc3NxqnzsyMpLPP/8cgL179zJixAjKysruNdVCCCGEEELUW1JgPUAjRowgPT2dwsLC\nCsc7d+5McnIyaWlpeHt7s2LFCuVcbm4uSUlJJCQkMHXqVNzc3EhPT8fMzIw9e/ag0+mIjIwkLi6O\n1NRU/P39iY2Nva+4du7cyfLly1m+fDnW1tZERUURGBjI5s2biY+P58MPPwRg5cqVzJkzB61Wy7p1\n6zAzM6tyzKNHjxIeHk5GRgZarZaTJ0+SkpJCQEAAa9asqfa5J0+eTEZGBj/88AORkZFER0fXasMM\nIYQQQgghHhZpcvEAWVhY4Ovry+rVqysUJ+fPnyc0NJSLFy9SUlKCnZ2dcs7d3R21Wo2joyN6vR53\nd3cAHB0dycvLIycnh+PHjxMcHAxAWVkZNjY29xzTDz/8QHZ2NitXrsTCwgKA/fv3c+LECeWa69ev\nU1RURKdOnViwYAE+Pj689tprNG7cuMpxnZ2dsbW1BcDe3p5XX31ViTszM7Pa527UqBHz5s3jrbfe\nYsaMGdjb29/z8wghhBBCCFGfSYH1gAUGBuLn51dhE97IyEiCgoLw8PAgMzNTeT0Oyr+7BWBkZIRa\nrUalUimf9Xo9BoMBBwcHNm7c+Jfisbe35/Tp0+Tk5ODs7AyUF2nJycmYmppWuHbs2LH07NmTPXv2\nMGzYMFasWEGbNm0qHfd23LdjvfM59Hp9jc99/PhxrKysqn3dUQghhBBCiEeNFFgPmJWVFZ6enqSk\npODv7w9AYWEhLVq0AMr3kbofrVu3pqCggKysLFxcXNDpdJw8eRIHB4d7uv+pp55i6tSpTJw4kc8+\n+wwHBwe6d+/OmjVrePvttwE4cuQIzz//PLm5uTg5OeHk5ER2djY5OTlVFlj3oqrnPnPmDKtWrWLL\nli2MHTuWvn370qFDh2rHMhgM0ia8julL9HUdghBCCCFEvScF1kMwevRo1q1bp3yeMGECISEhNGnS\nBDc3N/Ly8u55LI1GQ1xcHJGRkRQWFqLX6wkMDFQKrLlz5zJ//nwAnnzySRYvXnzXGG3atCEmJoaQ\nkBCWLVvGrFmziIiIwMfHB71ej6urKxERESQlJZGZmYlKpcLBwUF5XfGvquy5DQYDs2bNYtq0abRo\n0YKoqChmzJhBSkrKXStqdzIY4NKlwirPiwejoeysLoQQQghRV1QGg8FQ10EIURODwaC8Pilqn76k\nlIKrN+o6jAZDCtnaIXmuHZLn2iF5rh2S59rRUPJsY2NZ6XFZwRKPBJVKRf5n/67rMB5bLUJeresQ\nhBBCCCEeCdIb+w5OTk4sWLBA+ZyYmEh8fPxDnTMsLIxt27ZVOJafn8+kSZMASE1NJSIi4qHGUJVj\nx44p+2Ld/jNkyJBKr+3Tp4+yyfKbb75Zm2EKIYQQQghRb8gK1h00Gg07duxg7NixWFtb11kcLVq0\nIC4urs7mv83JyQmtVnvf923YsOEhRCOEEEIIIUT9JwXWHUxMTBg6dChJSUmEhoZWOLd7924SEhLQ\n6XRYWVkRExND8+bNiY+PJy8vj9OnT3Pu3DlmzJjBoUOH2Lt3L7a2tv+/vTuPqrrMHzj+ZrmACKPg\ngiuNC6CmJALhNGoNuF+uKOgvzRxcfmObxvgjlzB1MEwp0oIIMzVxqZFB4IZI5jIuU0rooRInxfyh\ncDUxo18gGMv1+/uD43dEIFHowrXP6xzP6bs+n+fTPcDnPM99HtavX49GoyE3N5c1a9ZQXl6Ok5MT\nq1evVveRupPBYODZZ59l9+7dtc4fOnSIhIQEEhISgJoFLi5fvgxAREQE3t7efPHFF6xatQqomVa3\nfft2df+r22VlZREXF4ejoyN5eXmMGzcOd3d3tm7dSkVFBfHx8bi6ulJcXFxvOz/++CPh4eEUFRUx\nePBgbv8qn5eXFzk5OZSVlfH8889TUlJCdXU1YWFhjBw5EoPBwF/+8he8vb3JycnBxcWFd9999xc3\nNhZCCCGEEMIcyBTBO0yfPp309HRKS2uvWOft7U1SUhJpaWlotVo2btyoXisoKCAxMZGEhAQWLlyI\nn58f6enp2NnZcfjwYaqqqoiKiiI2NpaUlBRCQkJYt27dPcW1b98+NmzYwIYNG3B2dmbVqlWEhoay\na9cu4uLieOWVVwDYvHkzy5cvR6/Xs2PHjl8sWs6cOUNkZCSZmZno9XouXLhAcnIykydPZtu2bQAN\nthMfH8+QIUPIyMhg1KhRagF2O1tbW+Lj40lNTSUxMZHo6Gi1ELt48SLTp08nIyMDR0dH9u7de0/5\nEEIIIYQQojWSEaw7ODg4EBQUxNatW2sVJ1euXGHBggV8//33VFZW0qNHD/XaiBEj0Gg0uLu7YzQa\n1eXN3d3dMRgM5Ofnk5eXx6xZs4CajX47derU6JiOHz9Obm4umzdvVkejPv/8c7799lv1nuvXr1NW\nVsaQIUNYs2YNOp2O0aNH07Zt2wbfO2jQIHUUzdXVlT/+8Y9q3FlZWb/YTnZ2trpx8BNPPEG7du3q\nvF9RFNauXUt2djaWlpYUFRVx7do1AHr06EH//v0BePjhh7l06VKj8yGEEEIIIURrJQVWPUJDQwkO\nDiY4OFg9FxUVxcyZMwkICCArK0stLqDmu1sAlpaWaDQadTlxS0tLjEYjiqLg5ubGzp077yseV1dX\nCgsLyc/PZ9CgQUBNkZaUlFRn76i5c+fy+OOPc/jwYaZNm8bGjRsb3Cz4Vty3Yr29H0aj8RfbaYz0\n9HSKi4tJSUlBo9Hg7+9PRUVFnbatrKzU80IIIYQQQpgzKbDq0b59e8aOHUtycjIhISEAlJaW4uLi\nAkBaWto9va9Xr14UFxeTk5ODl5cXVVVVXLhwQd0s+G66devGwoULmT9/Pm+//TZubm4MGzaMbdu2\n8d///d8AfPPNN/Tv35+CggI8PDzw8PAgNzeX/Pz8BgusxmioHV9fX9LT03n+OT6zowAAH4BJREFU\n+ec5fPgwP/30U51nS0tL6dChAxqNhuPHjzdplEpRFFkqvAUZK6tbOgQhhBBCCLMgBVYDZs+ezY4d\nO9TjefPmERYWRrt27fDz88NgMDT6XTY2NsTGxhIVFUVpaSlGo5HQ0FC1wFqxYgWvvfYaAF27duXN\nN9+s844+ffoQExNDWFgY69evZ+nSpaxcuRKdTofRaMTHx4eVK1eSmJhIVlYWFhYWuLm5qdMV71dD\n7bzwwguEh4ej1Wrx8vKiW7dudZ7V6XQ899xz6HQ6Bg4cSO/eve87DkWBa9dK736jaJIHZeM/IYQQ\nQoiWYqHcvvybEK2Uoijq1EthOsbKaop/utHSYTxwpJA1DcmzaUieTUPybBqSZ9N4UPLcqZNjvedl\nBEuYBQsLC67GHWzpMH5zOs/3b+kQhBBCCCHMitks0+7h4cGaNWvU402bNhEXF/ertrlkyRL8/f0J\nCgpi0qRJ5OTkNHsbXl5e9/zMX/7yF0pKSigpKak1jbE+Z8+eJSgoqNa/KVOm3G+4DbrVj6KiIl58\n8cVmf78QQgghhBDmwGxGsGxsbPj000+ZO3cuzs7OJmt30aJFjB07ln/9618sX76c9PR0k7V9J0VR\nUBSF999/H6jZkPijjz5i+vTpDT7j4eGBXq83VYi4uLgQGxtrsvaEEEIIIYRoTcymwLK2tubJJ58k\nMTGRBQsW1Lp28OBBEhISqKqqon379sTExNCxY0fi4uIwGAwUFhby3Xff8fLLL/Pll19y9OhROnfu\nzPr169FoNOTm5rJmzRrKy8txcnJi9erV6v5Qt/j6+lJQUADUrKS3YsUKbty4gaurK6+99hrt2rVj\nxowZeHh4kJ2djdFo5LXXXsPT05O4uDjs7e2ZM2cOAIGBgaxfv77WXlplZWU8//zzlJSUUF1dTVhY\nGCNHjsRgMDBnzhweeeQRTp8+zYYNG5gxYwbJycm8+eabFBQUEBQUxGOPPcYPP/zA6NGjGTlyJADh\n4eGMGzdOPb5dSkoK+/fv58aNG1y8eJHZs2dTVVWFXq/HxsaGDRs20L59ewoKCoiMjOTHH3/Ezs6O\nV199lT59+lBYWMhLL71EeXk5/v7/mUZmMBh49tln2b17NwaDgUWLFnHjRs13eJYtW8aQIUPUZe6d\nnJzIy8vj4YcfJiYmRr5jJYQQQgghzJ7ZTBEEmD59Ounp6ZSW1l5Nztvbm6SkJNLS0tBqtWzcuFG9\nVlBQQGJiIgkJCSxcuBA/Pz/S09Oxs7Pj8OHDVFVVERUVRWxsLCkpKYSEhLBu3bo6bR88eBB3d3eg\nZlTrpZdeIj09HXd391p7Yv3888/o9XpWrFhBREREo/tma2tLfHw8qampJCYmEh0dza31Ry5evMhT\nTz1FRkYG3bt3V58JDw/H1dUVvV7P4sWLmTx5MikpKUDNEuk5OTk88cQTDbZ57tw54uLiSE5OZt26\nddjZ2ZGWlsbgwYPVpeiXLVvGsmXLSElJYfHixURGRgKwatUqpk2bRnp6ep1i9JYOHTrwwQcfkJqa\nyrp164iKilKv/fvf/yYiIoI9e/ZgMBg4efJko3MlhBBCCCFEa2U2I1gADg4OBAUFsXXrVuzs7NTz\nV65cYcGCBXz//fdUVlbWGhkaMWIEGo0Gd3d3jEajumy5u7s7BoOB/Px88vLymDVrFlCzsW6nTp3U\n519//XUSEhJwdnZm1apVlJaWUlpayqOPPgrApEmTCAsLU+/XarVAzYjX9evXKSkpaVTfFEVh7dq1\nZGdnY2lpSVFREdeuXQNq9sEaPHjwXd/x6KOPEhkZSXFxMXv37mXMmDFYWzf8v9jPzw8HBwcAHB0d\n1ZEod3d3zp49S1lZGTk5ObX6V1lZCUBOTo76HbigoCBiYmLqvL+6upqVK1dy5swZLC0tuXDhgnrN\n09OTLl26ANCvXz8uXbqEj4/PXfsohBBCCCFEa2ZWBRZAaGgowcHBBAcHq+eioqKYOXMmAQEB6vSz\nW2xsbACwtLREo9Go09AsLS0xGo0oioKbmxs7d+6st71b38G65c7RszvdOc3NwsICKysrbt68qZ6r\nqKio81x6ejrFxcWkpKSg0Wjw9/dX77O3t//FNm8XFBTExx9/TEZGBqtXr/7Fe2/lBv6Tn1v/fSs3\nv/vd7xr8DtfdpvRt2bKFjh07otfruXnzJp6envW2bWVlhdFovGvfhBBCCCGEaO3MrsBq3749Y8eO\nJTk5mZCQEKCm6HFxcQFQp7Y1Vq9evSguLiYnJwcvLy+qqqq4cOGCugnwnRwdHfnd737HiRMn8PHx\nQa/X4+vrq17fs2cPQ4cO5cSJEzg6OuLo6Ej37t05dOgQAKdPn653k+LS0lI6dOiARqPh+PHjXLp0\n6a6xt23blrKyslrngoODmTJlCh07dqRv3773kIm6HBwc6NGjB5mZmYwbNw5FUTh79iz9+vXDy8uL\njIwMtaCrT2lpKV26dMHS0pLU1NQmFVGKosiS4S3AWFnd0iEIIYQQQpgVsyuwAGbPnl1refJ58+YR\nFhZGu3bt8PPzq7eAaYiNjQ2xsbFERUVRWlqK0WgkNDS0wQILIDo6Wl3komfPnrVGimxtbZk4cSLV\n1dW89tprAIwZMwa9Xo9Wq8XT05Pf//73dd6p0+l47rnn0Ol0DBw4kN69e981dicnJ4YMGUJgYCDD\nhw9n8eLFdOzYkd69e9e7sMX9eOONN/jb3/5GQkIC1dXVjB8/nn79+rF06VJeeuklNm7cWGuRi9s9\n9dRTzJ8/n7S0NIYPH35PI3F3UhS4du2XRw9F0z0oG/8JIYQQQrQUC+XWSgqiyWbMmMGiRYsYNGhQ\ni8Vw48YNdDodqampODrWv7u0Obp5U+GHH663dBgPPCmwTEPybBqSZ9OQPJuG5Nk0JM+m8aDkuVOn\n+v/WNssRLFG/zz//nKVLlxIaGvpAFVcAFhYNf4hF87qVZ2NlNcU/3WjhaIQQQgghzMtvssDq37+/\nuqpg7969iY6Opk2bNk1+77Zt24CaPaZyc3NZvnx5o589deoUer2eV155haysLDQaDUOGDLmn9h97\n7DH8/PzYunUrqampAAwePJgvv/yy1n09evQgPj4eAH9/f5KTk3F2dsbLy4ucnJx7atNULCwsuPrO\nnpYO4zel87zxLR2CEEIIIYTZ+U0WWHZ2durKeOHh4fz9739Xl2lvCdXV1QwaNEidWvjFF19gb29/\nzwXWLXeufNjaGI1GrKysWjoMIYQQQgghmp1ZbTT8a/Dx8eHixYsAfPDBBwQGBhIYGMiWLVsAMBgM\njB07lvDwcMaNG8eLL77IjRs106b8/f0pLi4GakagZsyYUef9Bw8eZMqUKUycOJGZM2eqe1vFxcWx\ncOFCpk6dyqJFi8jKyuKZZ57BYDDw97//nS1bthAUFMSJEyfw9/enqqoKgOvXr9c6bqzdu3ej0+kI\nDAzkjTfe+MV7FUUhOjqawMBAdDode/bUjBxFRkZy4MABAF544QVefvllAHWjYgC9Xs/kyZMJCgpi\n+fLl6sqBXl5erFmzhgkTJpCTk0NMTAzjx49Hp9MRHR19T30RQgghhBCitfpNF1jV1dUcOXIEd3d3\ncnNzSUlJISkpiZ07d/KPf/yDf//73wDk5+fz1FNPkZmZSdu2bfnwww8b3Ya3tzdJSUmkpaWh1WrZ\nuHGjeu38+fNs2bKFtWvXqud69OjB1KlTmTlzJnq9Hh8fH/z8/Dh8+DAAGRkZjB49Wt2zqj6vv/46\nQUFBBAUFcfbsWYqKioiJiSExMZG0tDROnTrF/v37G3z+008/5cyZM+j1ej744ANef/11rl69io+P\nDydOnACgqKiI8+fPA3Dy5El8fHw4f/48mZmZfPTRR+j1eiwtLUlPTwegvLwcT09PPv74Y/r06cO+\nffvIyMggPT2d5557rtH5FEIIIYQQojX7TRZYP//8M0FBQYSEhNCtWzcmT57MyZMnGTlyJPb29rRt\n25ZRo0apxUTXrl3x9vYGYMKECZw8ebLRbV25coU5c+ag0+nYuHEj586dU6/5+/tjZ2d313dMnjyZ\nXbt2ATXf77p9k+X6LFq0CL1ej16vx8PDg1OnTvHoo4/i7OyMtbU1Op2O7OzsBp8/efIkWq0WKysr\nOnbsiK+vL6dOncLHx4eTJ0/y7bff0rdvXzp06MDVq1fVPcSOHTtGbm6uOoJ17NgxCgsLgZrNhMeM\nGQPU7CVma2tLREQEn376aaNyIIQQQgghhDn4zX8HqzEsLCzqPbaysuLWKvcVFRX1PhsVFcXMmTMJ\nCAggKyuLd955R73W2IU1vL29iYyMJCsrC6PRiLu7e6Njb04uLi6UlJRw9OhRfHx8+Omnn8jMzMTe\n3h4HBwcURWHSpEmEh4fXedbW1lb93pW1tTXJyckcO3aMTz75hO3bt7N161ZTd0cIIYQQQohm95sc\nwaqPj48P+/fv58aNG5SXl7N//358fHwAuHz5srq63u7du9XRrO7du5ObmwvUTKurT2lpKS4uLgCk\npaU1Kpa2bdtSVlZW69zEiRMJDw+/6+hVfTw9PcnOzqa4uBij0UhGRga+vr4N3u/j40NmZiZGo5Hi\n4mJOnDiBp6cnULMqYWJiIr6+vvj4+LB582Y1T3/4wx/Yu3cvP/zwAwD/93//x6VLl+q8v6ysjNLS\nUh5//HEiIiI4e/bsPfdJCCGEEEKI1ug3OYJVn4cffpjg4GCmTJkC1EzLGzBgAAaDgV69erFjxw4i\nIiLo27cv06ZNA2DevHksXbqUt99+Gz8/v3rfO2/ePMLCwmjXrh1+fn4YDIa7xvKnP/2JF198kQMH\nDrBs2TJ8fHzQ6XS89dZbBAYG3nPfOnfuTHh4OKGhoSiKwuOPP87IkSMbvH/UqFHk5OQQFBSEhYUF\nCxcupFOnTkDNaNq//vUvHnroIbp168ZPP/2kFlh9+/blr3/9K7Nnz+bmzZtoNBqWL19O9+7da72/\nrKyM559/Xh31W7JkyV37oCiKLBtuYsbK6pYOQQghhBDC7Fgot+a4iXoZDAaeffZZdu/e3aJxfPLJ\nJxw4cOCuKwA+qG7eVPjhh+stHcYD70HZWb21kzybhuTZNCTPpiF5Ng3Js2k8KHnu1Mmx3vMygmUG\nXn31VY4cOcKGDRtaOpQWY2HR8IdYNK9OnRwxVlZT/NONlg5FCCGEEMLstNoCy8PDg1mzZqnTxzZt\n2kR5eTnz58//VdvdtGkT//jHP7C1tcXa2poZM2a0+OjVsmXL6pyLjIwkOzubkpISnJycAPjzn/9M\nSEiIqcNjyZIlPPHEE4wdO5alS5cya9Ys+vbt26xtWFhYcDU+pVnfKRrW+YV7/66fEEIIIYRoxQWW\njY0Nn376KXPnzsXZ2dkkbX700Ud8/vnnJCcn4+DgwPXr19m3b59J2r5XK1asUKcv3suKiL+2VatW\ntXQIQgghhBBCtJhWW2BZW1vz5JNPkpiYyIIFC2pdO3jwIAkJCVRVVdG+fXtiYmLo2LEjcXFxGAwG\nCgsL+e6773j55Zf58ssvOXr0KJ07d2b9+vVoNBpyc3NZs2YN5eXlODk5sXr1ajp37sx7773Htm3b\ncHBwAMDBwYFJkyYBcOzYMaKjozEajQwcOJDIyEhsbGzw9/dHq9Vy5MgRrKysePXVV1m7di0XL15k\nzpw5TJs2jaysLOLi4nB0dCQvL49x48bh7u7O1q1bqaioID4+HldXV4qLi1mxYgWXL18GICIiAm9v\nb+Li4rh8+TIGg4HLly8TGhrKn//8Z958800KCgoICgriscceY9asWSxYsIDr169jNBr529/+pi5A\ncScvLy+mTp3KkSNH6NSpE//zP//DG2+8weXLl4mIiCAgIACj0UhMTAxffPEFlZWVTJ8+nalTp6Io\nCq+++iqfffYZXbt2rbXp8YwZM1i0aBGDBg1ixYoVnDp1ioqKCsaMGcOLL74I1Oz/NXHiRP75z39S\nXV3NW2+9RZ8+fZr9MySEEEIIIYSptepl2qdPn056ejqlpaW1znt7e5OUlERaWhparZaNGzeq1woK\nCkhMTCQhIYGFCxfi5+dHeno6dnZ2HD58mKqqKqKiooiNjSUlJYWQkBDWrVvH9evXKSsro2fPnnXi\nqKioYMmSJaxbt4709HSMRiMffviher1r167o9Xp8fHxYsmQJb7/9NklJScTFxan3nDlzhsjISDIz\nM9Hr9Vy4cIHk5GQmT57Mtm3bgJrRn9DQUHbt2kVcXByvvPKK+nx+fr46fTE+Pp6qqirCw8NxdXVF\nr9ezePFidu/ezbBhw9RNhvv169dgbsvLyxk6dCgZGRm0bduWt956i82bNxMfH09sbCwAycnJODo6\nsmvXLnbt2kVSUhKFhYXs27eP/Px89uzZQ3R0tLqE/Z0WLFhASkoKH3/8MdnZ2Zw5c0a95uTkRGpq\nKlOnTmXz5s0NximEEEIIIYQ5abUjWFAzghQUFMTWrVuxs7NTz1+5coUFCxbw/fffU1lZSY8ePdRr\nI0aMQKPR4O7ujtFoZMSIEQC4u7tjMBjIz88nLy+PWbNmAXDz5k11CfKG5Ofn06NHD3r16gXApEmT\n2LFjBzNnzgQgICBAbaO8vFwdAbOxsaGkpASAQYMG0blzZwBcXV354x//qD6TlZUFwOeff863336r\ntnur6AN4/PHHsbGxwdnZGWdnZ3WvqdsNGjSIiIgIqqurGTlyJP3792+wTxqNplZubGxs1Lzd2rvq\ns88+4+zZs+zduxeo2dPr4sWLZGdno9VqsbKywsXFhaFDh9bbRmZmJklJSVRXV/P9999z/vx5tegb\nPXo0AAMHDmy10zCFEEIIIYS4V626wAIIDQ0lODi41ga7UVFRzJw5k4CAALKysnjnnXfUazY2NgBY\nWlqi0WiwsLBQj41GI4qi4Obmxs6dO+u0ZW9vT2FhYb2jWL/k1hQ5S0tLtf1bx9XV1bXiuvO+W3FB\nTbGXlJSEra1tnTZuf97Kykp97+18fX3Zvn07hw8fZsmSJcyaNYuJEyc2GPPtuakvHkVReOWVVxg+\nfHitZw8fPvxL6QCgsLCQzZs3k5ycTLt27ViyZIm679Wt9u9sTwghhBBCCHPXqqcIArRv356xY8eS\nnJysnistLcXFxQWAtLS0e3pfr169KC4uVqe1VVVVce7cOQDmzp1LZGQk16/X7LdUVlZGWloavXr1\n4tKlS1y8eBEAvV6Pr69vk/t2p2HDhqnTBQG++eabX7y/bdu26ggXwKVLl+jYsSP/9V//xZQpUzh9\n+nST4/noo4+oqqoCakbyysvL8fX1JTMzE6PRyNWrV9URuNuVlZXRpk0bHB0duXbtGkeOHGlSLEII\nIYQQQpiDVj+CBTB79mx27NihHs+bN4+wsDDatWuHn58fBoOh0e+ysbEhNjaWqKgoSktLMRqNhIaG\n4ubmxlNPPUV5eTkhISFoNBqsra2ZNWsWtra2rF69mrCwMHWRi2nTpjV7P5cuXcrKlSvR6XQYjUZ8\nfHxYuXJlg/c7OTkxZMgQAgMDGT58OO7u7mzatAlra2vs7e2Jjo5uUjxTpkzh0qVLBAcHoygKTk5O\nvPvuu4waNYrjx48zfvx4unXrxuDBg+s8269fPwYMGMC4cePo0qULQ4YMaVIsiqLI0uEmZKysO0Iq\nhBBCCCHuzkJRFKWlgxBCCCGEEEKIB0GrnyIohBBCCCGEEObCLKYIivs3ZcoUKisra517/fXX8fDw\naKGIhBBCCCGEeHDJFEEhhBBCCCGEaCYyRVAIIYQQQgghmokUWEIIIYQQQgjRTKTAEkIIIYQQQohm\nIgWWEEIIIYQQQjQTKbBEizty5Ahjxoxh1KhRbNiwoc71yspK/vrXvzJq1CimTJlSa2Pp9957j1Gj\nRjFmzBiOHj1qyrDNzv3m+ccff2TGjBl4eXn94sbXosb95vmzzz4jODgYnU5HcHAwx44dM3XoZuV+\n8/z1118TFBREUFAQEyZMYN++faYO3aw05eczwOXLl/Hy8mLTpk2mCtks3W+eDQYDnp6e6md6+fLl\npg7drDTl83zmzBmefPJJtFotOp2OiooKU4ZuVu43zx9//LH6WQ4KCqJfv3588803pg6/eShCtKDq\n6molICBAKSgoUCoqKhSdTqecO3eu1j3bt29Xli1bpiiKouzevVsJCwtTFEVRzp07p+h0OqWiokIp\nKChQAgIClOrqapP3wRw0Jc9lZWVKdna28uGHHyqRkZEmj92cNCXPp0+fVq5cuaIoiqKcPXtWGTZs\nmGmDNyNNyXN5eblSVVWlKIqiFBUVKUOHDlWPRW1NyfMt8+fPV+bPn69s3LjRZHGbm6bkubCwUNFq\ntSaP2Rw1Jc9VVVVKYGCg8s033yiKoijFxcXy90YDmuPnhqIoypkzZ5SAgACTxPxrkBEs0aK+/vpr\nHnroIXr27ImNjQ1arZYDBw7UuufgwYNMmjQJgDFjxnDs2DEUReHAgQNotVpsbGzo2bMnDz30EF9/\n/XVLdKPVa0qe7e3t8fHxwdbWtiVCNytNyfOAAQNwcXEBwM3NjYqKijp72IkaTclzmzZtsLau2QKy\noqICCwsLk8dvLpqSZ4D9+/fTvXt33NzcTB67OWlqnkXjNCXPn332GR4eHvTr1w8AJycnrKysTN4H\nc9Bcn+eMjAy0Wq3J4m5uUmCJFlVUVESXLl3UYxcXF4qKiurc07VrVwCsra1xdHTkxx9/bNSzokZT\n8iwar7nyvHfvXgYMGICNjc2vH7QZamqev/rqK7RaLRMmTCAyMlItuERtTclzWVkZ77//PvPmzTNp\nzOaoqZ9ng8HAxIkTefrppzlx4oTpAjczTclzfn4+FhYWzJkzh0mTJvH++++bNHZz0ly/B/fs2WPW\nBZb8VhFCiFbk3LlzxMTEsHnz5pYO5YH1yCOPkJGRwfnz51m8eDEjRoyQEdpm9s477xAaGkrbtm1b\nOpQHWufOnfnnP/+Jk5MTubm5vPDCC2RkZODg4NDSoT1QjEYjJ0+eJDk5mTZt2jBz5kwGDhzIH/7w\nh5YO7YH01Vdf0aZNG9zd3Vs6lPsmI1iiRbm4uHDlyhX1uKioSJ0mdfs93333HQDV1dWUlpbi5OTU\nqGdFjabkWTReU/N85coV5s2bR3R0NK6urqYL3Mw01+e5T58+2Nvbk5eX9+sHbYaakuevvvqKmJgY\n/P39SUxM5L333mP79u0mjd9cNCXPNjY26ud64MCBuLq6kp+fb7rgzUhT8tylSxd8fX1xdnamTZs2\njBgxgtOnT5s0fnPRHD+fzX16IEiBJVrYoEGDuHDhAoWFhVRWVpKRkYG/v3+te/z9/UlNTQVqpk4N\nHToUCwsL/P39ycjIoLKyksLCQi5cuICnp2dLdKPVa0qeReM1Jc8lJSXMnTuX8PBwvL29WyJ8s9GU\nPBcWFlJdXQ3ApUuX+N///V+6d+9u8j6Yg6bk+cMPP+TgwYMcPHiQ0NBQnnnmGZ5++umW6Ear15Q8\nFxcXYzQaAdTfgz179jR5H8xBU/I8bNgw8vLyuHHjBtXV1WRnZ9O3b9+W6Ear19S/N27evElmZqbZ\nF1iyiqBocYcOHVJGjx6tBAQEKO+++66iKIry1ltvKfv371cURVF+/vlnZf78+crIkSOVkJAQpaCg\nQH323XffVQICApTRo0crhw4dapH4zUVT8vynP/1J8fX1VQYPHqwMHz68zopA4j/uN8/x8fHKI488\nokyYMEH9d+3atRbrR2t3v3lOTU1Vxo8fr0yYMEGZOHGism/fvhbrgzloys+NW2JjY2UVwbu43zx/\n8skntT7PBw4caLE+mIOmfJ7T0tKU8ePHK1qtVomOjm6R+M1FU/J8/PhxZcqUKS0Sd3OyUBRZhkYI\nIYQQQgghmoNMERRCCCGEEEKIZiIFlhBCCCGEEEI0EymwhBBCCCGEEKKZSIElhBBCCCGEEM1ECiwh\nhBBCCCGEaCZSYAkhhBBmaMaMGRw9erTWuS1btrBixYoGn/Hy8vq1wxJCiN88KbCEEEIIMxQYGMie\nPXtqnduzZw+BgYEtFJEQQgiQAksIIYQwS2PGjOHQoUNUVlYCYDAYuHr1Kv379yc0NJRJkyah0+nY\nv39/nWezsrJ45pln1OOVK1eSkpICQG5uLk8//TTBwcHMmTOHq1evmqZDQgjxgJACSwghhDBD7du3\nx9PTkyNHjgA1o1fjxo3Dzs6O+Ph4UlNTSUxMJDo6GkVRGvXOqqoqoqKiiI2NJSUlhZCQENatW/dr\ndkMIIR441i0dgBBCCCHuj1arZc+ePYwcOZKMjAxWrVqFoiisXbuW7OxsLC0tKSoq4tq1a3Tq1Omu\n78vPzycvL49Zs2YBcPPmzUY9J4QQ4j+kwBJCCCHMVEBAAKtXr+b06dP8/PPPDBw4kJSUFIqLi0lJ\nSUGj0eDv709FRUWt56ysrLh586Z6fOu6oii4ubmxc+dOk/ZDCCEeJDJFUAghhDBTbdu2xc/Pj4iI\nCLRaLQClpaV06NABjUbD8ePHuXTpUp3nunfvzvnz56msrKSkpIRjx44B0KtXL4qLi8nJyQFqpgye\nO3fOdB0SQogHgIxgCSGEEGYsMDCQF154gbVr1wKg0+l47rnn0Ol0DBw4kN69e9d5pmvXrowdO5bA\nwEB69OjBgAEDALCxsSE2NpaoqChKS0sxGo2Ehobi5uZm0j4JIYQ5s1Aa+81XIYQQQgghhBC/SKYI\nCiGEEEIIIUQzkQJLCCGEEEIIIZqJFFhCCCGEEEII0UykwBJCCCGEEEKIZiIFlhBCCCGEEEI0Eymw\nhBBCCCGEEKKZSIElhBBCCCGEEM3k/wF+Zrb4Ayos6AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mgmnrHiyoJJw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "del model_xgb\n",
        "model_xgb = xgb.XGBRegressor(learning_rate=0.1,\n",
        "                          n_estimators=4000,\n",
        "                          verbosity=3,\n",
        "                          objective='reg:squarederror',\n",
        "                          n_jobs=-1,\n",
        "                          subsample=0.95,\n",
        "                          random_state=0,\n",
        "                          tree_method='gpu_hist')\n",
        "score = rmsle_cv(model_xgb)\n",
        "print(\"Xgboost score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))\n",
        "#Xgboost score: 785013.5222 (119072.6154)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i9PUOBsnb33S",
        "colab_type": "text"
      },
      "source": [
        "#### GradientBoostingRegressor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ExELU9ZHoIo0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        },
        "outputId": "d29f879a-904d-4c09-d50c-4c788f8d23cb"
      },
      "source": [
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "\n",
        "model_gb = GradientBoostingRegressor(learning_rate=0.2,\n",
        "                               n_estimators=1500,\n",
        "                               subsample=0.8,\n",
        "                               random_state=0,\n",
        "                               verbose=1)\n",
        "model_gb.fit(X_train, y_train)\n",
        "\n",
        "y_pred = model_gb.predict(X_dev)\n",
        "print('RMSE', sqrt(mean_squared_error(y_dev, y_pred)))"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "      Iter       Train Loss      OOB Improve   Remaining Time \n",
            "         1 10853365845117.2188 4959463295419.4355           12.11m\n",
            "         2 7468982465170.4502 3036599422792.6533           11.89m\n",
            "         3 5103010224344.0000 2084271094793.9810           11.84m\n",
            "         4 3399309109491.2583 1304942024811.2061           11.82m\n",
            "         5 2396955522520.6182 999831289959.0818           11.76m\n",
            "         6 1607873427214.1733 603445188821.9910           11.77m\n",
            "         7 1220808244648.7961 385815696622.3005           11.78m\n",
            "         8 892577762705.4023 289064471080.4836           11.79m\n",
            "         9 706552896591.7443 185151497574.1179           11.77m\n",
            "        10 528872307751.0637 152649190266.1892           11.80m\n",
            "        20 221429146860.8983  1042618105.1793           11.74m\n",
            "        30 165427154614.4551  -587760884.8721           11.68m\n",
            "        40 146260319414.6407   -34016149.6725           11.58m\n",
            "        50 121059270209.7304  -422667912.3304           11.49m\n",
            "        60 105964158482.9812  -968771552.0463           11.39m\n",
            "        70 100823360012.7487  -604945962.7433           11.31m\n",
            "        80 86094014089.0216  -910962469.5150           11.23m\n",
            "        90 79704173456.0479   419503389.5922           11.15m\n",
            "       100 70745381533.3743  -701558989.0433           11.07m\n",
            "       200 35403743101.7605    27043821.0196           10.29m\n",
            "       300 23239944745.8930   -60133166.9747            9.48m\n",
            "       400 17049665998.0278   -47833353.2706            8.68m\n",
            "       500 13407552502.7962   -15686671.0497            7.88m\n",
            "       600 11161673928.0145    -7944183.3618            7.08m\n",
            "       700  9201129319.1669    -4976016.8904            6.28m\n",
            "       800  7697713937.8845   -14183328.1588            5.49m\n",
            "       900  6621874111.0419   -10824587.6359            4.70m\n",
            "      1000  5932858366.8862    -9958959.3049            3.92m\n",
            "RMSE 444677.7577486528\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "agpvJnSMc4dx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "outputId": "8dc46b42-716d-4fa3-a7e6-0e28d5926f40"
      },
      "source": [
        "feature_imp = pd.DataFrame(sorted(zip(model_gb.feature_importances_, X_train.columns), reverse=True)[:50], columns=['Value','Feature'])\n",
        "plt.figure(figsize=(12,8))\n",
        "sns.barplot(x=\"Value\", y=\"Feature\", data=feature_imp.sort_values(by=\"Value\", ascending=False))\n",
        "plt.title('GradientBoostingRegressor Feature Importances')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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h+fLl/PPPP9y8eZN169blW37QoEEcPnyYM2fOoNVq8fLyIiAggLt37wJw8+ZNDh48CECX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\nIlJDVGCJiIiIiIjUkP8HITM9AkeoCxoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9CnUKqxRsJc4",
        "colab_type": "text"
      },
      "source": [
        "**Note:** GradientBoostingRegressor shows the most promise, so we will use multiple versions of this model and ensemble them to create the final predictions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dhF6kzkWoJMm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Note: will take a long time to run. Time will vary depending on your machine\n",
        "rmse = []\n",
        "preds = []\n",
        "fold = KFold(n_splits=10, shuffle=False, random_state=0)\n",
        "\n",
        "for train_idx, test_idx in fold.split(train):\n",
        "    X_train, X_dev = train.loc[train_idx], train.loc[test_idx]\n",
        "    y_train, y_dev = Y_train[train_idx], Y_train[test_idx]\n",
        "\n",
        "    model_gb = GradientBoostingRegressor(learning_rate=0.2,\n",
        "                                   subsample=0.8,\n",
        "                                   n_estimators=1500,\n",
        "                                   random_state=0)\n",
        "    model_gb.fit(X_train, y_train)\n",
        "    y_pred = model_gb.predict(X_dev)\n",
        "    print('RMSE', sqrt(mean_squared_error(y_dev, y_pred)))\n",
        "    rmse.append(sqrt(mean_squared_error(y_dev, y_pred)))\n",
        "\n",
        "    predictions = model_gb.predict(test)\n",
        "    preds.append(predictions)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jgLqLAteoJTB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print('Mean RMSLE = ', np.mean(rmse))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tqWEVlsXoJUw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "preds_iter_1 = np.mean(preds)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sqxNZ00tw6Vt",
        "colab_type": "text"
      },
      "source": [
        "**Note:** iterate through the folds and store the resultant predictions alongside the Unique IDs. The final ensemble was a hand-picked, normally weighted, additive combination of folds.\n",
        "\n",
        "-------------------------------------------------------------------------------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yjBx9CsryC8_",
        "colab_type": "text"
      },
      "source": [
        "This marks the end of the notebook for the hackathon! The below ideas are experimental, and therefore not used in the hackathon. However, MachineHack's customer for this hackathon may find some value in it.\n",
        "\n",
        "I have a strong feeling that the customer is [SoundCloud](https://soundcloud.com/pages/contact) due to the one-to-one correspondence of song titles in the data and the songs on the website. That said, the duration of songs could plausibly be a strong predictor of the number of views. Anecdotal evidence and published research on the subject supports this hypothesis.\n",
        "\n",
        "Of course, if the customer is SoundCloud, they have direct access to this data. Nevertheless, the code below demonstrates a proof-of-concept (PoC) of obtaining this data using the [SoundCloud API](https://developers.soundcloud.com/docs/api/guide). Bare in mind that doing so may violate SoundCloud's ToS and there is therefore not condoned by the author. It only serves as a benign PoC."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Li8j6kZOxwi3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install -q soundcloud\n",
        "song_titles = alldata.Song_Name.values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WsfZUc16oJZ8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import soundcloud\n",
        "\n",
        "# create a client object with your app credentials or alternatively inspect GET requests to the website from you browser for this parameter\n",
        "client = soundcloud.Client(client_id='xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx')\n",
        "\n",
        "durations = []\n",
        "for i in range(0, len(song_titles)):\n",
        "  try:\n",
        "    tracks = client.get('/tracks', q=song_titles[i])\n",
        "    if len(tracks) == 0:\n",
        "      durations.append('None')\n",
        "    else:\n",
        "      durations.append(tracks[0].duration)\n",
        "      if i % 1000 == 0:\n",
        "        print('[*]Downloaded durations: ', i)\n",
        "  except (Exception):\n",
        "    try:\n",
        "      tracks = client.get('/tracks', q=song_titles[i])\n",
        "      if len(tracks) == 0:\n",
        "        durations.append('None')\n",
        "      else:\n",
        "        durations.append(tracks[0].duration)\n",
        "        pass\n",
        "    except (Exception):\n",
        "      durations.append('None')\n",
        "      pass\n",
        "print('[✓]Download Complete...')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LE8TpH3roJcW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "song_length = pd.DataFrame(durations, columns=[\"duration\"])\n",
        "# The customer can combine the song durations feature along with the original dataset and observe model performance."
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}
